215 Mac courses in Edinburgh

This tour comprises of 5 nights and 5 full days visiting and photographing some of the most iconic locations used in the Outlander television series.

Java training course description A comprehensive introduction of the Java language and environment. It is important to note that the course will assume that the delegates are already familiar with the C language as this enables more advanced features of the Java language to be covered in the course. The course will also give an overview of areas related to programming in Java. What will you learn Describe the Java architecture. Write Java applets and applications. Debug Java programs. Examine existing code and determine its function. Use multimedia extensions, the awt, multithreading, exceptions within Java Java training course details Who will benefit: Those wishing to program in Java. Prerequisites: Complete C programming Duration 5 days Java training course contents Review of UNIX fundamentals What is Java? What Java is, history of Java, reasons for success. The Java Virtual Machine, Bytecodes, getting up and running with Java, Java resources. Simple Java applications. C features in Java Java data structures, Java flow control, differences from C, arrays, strings and packages. OO features in Java Java classes and objects, inheritance, overloading, packages. Differences from C++. Java applets Applications vs. applets, HTML, the applet tag, applet methods, life cycle, testing and debugging. Multimedia applets Images, sounds, fonts, colours and animation. Java products The JDK in detail, other development environments. Javabeans and JDBC overviews. Abstract Window Toolkit JFC and Swing versus AWT. Event handling (JDK 1.1), GUIs, panels, buttons, lists, scrollbars, text areas, frames… Exception handling and multithreading Handling exceptions. Starting, pausing, stopping threads, producers, consumers, monitoring. More standard classes Java file I/O, Streams, The system class. The networking model, java.net classes. Security and Java Types of attack, the security manager, craplets, securing the network. Integrating legacy code with Java

Complete C programming training course description A hands-on introduction to programming in the ANSI C programming language. The course initially moves at a fast pace in order to spend as much time as possible on the subject of pointers - the area which cause the most bugs in C programs. What will you learn Write ANSI C programs Use the C libraries Debug C programs Examine existing code and determine its function. Complete C programming training course details Who will benefit: Programmers wishing to learn C. Programmers wishing to learn C++ or Java. Prerequisites: None, although experience in another high level language would be useful. Duration 5 days Complete C programming training course contents Getting started The compilation process, comments, main(), statement blocks, printf(). C data types and operators char, int, float and double, qualifiers, arithmetic and assignment operators, precedence, Associativity. Basic I/O C libraries, stdin and stdout, getchar(), putchar(), printf() formatting. Flow control if else, dangling elses, else if, while and for loops. switch statements, the null statement, break, continue and gotos. Functions Function calls, arguments and return types, function declarations (prototypes), function definitions, scope of variables. The preprocessor Preprocessor actions, macros, #include. Libraries and their relationship with header files. Conditional compilation. More data types and operators Logical, bitwise and other operators, type conversion, casting, typedefs and access modifiers. Arrays Declaring and handling arrays, common gotchas, multidimensional arrays. Pointers What are pointers? Why they are so important, declaring and using pointers,The three uses of the *,pointer example - scanf, pointers as arguments. More pointers Golden rules of pointers and arrays, pointers to arrays, pointer arithmetic, arrays of pointers, multiple indirection. Character/string manipulation Arrays of characters, string definition, working with strings, String library. Program arguments argc and argv, example uses,char *argv[] versus char ** argv. Program structure and storage classes Globals (externals), multi source programs, the look of a C program. Structures Declaration, the . and - operators, unions and bitfields. Library functions File handling, fopen and fclose, reading from and writing to files, fseek().calloc() and malloc()

Network DevOps course description This course is not a soft skills course covering the concepts of DevOps but instead concentrates on the technical side of tools and languages for network DevOps. Particular technologies focussed on are ansible, git and Python enabling delegates to leave the course ready to starting automating their network. Hands on sessions follow all major sections. More detailed courses on individual aspects of this course are available. What will you learn Evaluate network automation tools. Automate tasks with ansible. Use git for version control. Use Python to manage network devices. Use Python libraries for network devices. Network DevOps course details Who will benefit: Administrators automating tasks. Prerequisites: TCP/IP Foundation Duration 5 days Network DevOps course contents What is DevOps Programming and automating networks, networks and clouds, AWS, OpenStack, SDN, DevOps for network operations. Initial configuration Configuring SSH, ZTP, POAP. Hands on Initial lab configuration. Getting started with ansible The language, the engine, the framework. Uses of ansible, orchestration. The architecture, Controlling machines, nodes, Agentless, SSH, modules. Configuration management, inventories, playbooks, modules, roles. Hands on Installing ansible, running ad hoc commands. Ansible playbooks ansible-playbook, YAML, plays, tasks, handlers, modules. Playbook variables. Register module, debug module. Hands on Running playbooks. Ansible Inventories /etc/ansible/hosts, hosts, groups, static inventories, dynamic inventories. Inventory variables, external variables. Limiting hosts. Hands on Static inventories, variables in inventory files. Ansible modules for networking Built in modules, custom modules, return values. Core modules for network operations. Cisco and/or Juniper modules. ansible_connection. Ansible 2.6 CLI. Hands on Using modules. Ansible templating and roles aConfiguration management, full configurations, partial configurations. The template module, the assemble module, connection: local, Jinja2 templates, variables, if, for, roles. Hands on Generating multiple configurations from a template. Network programming and modules Why use Python? Why use ansible? alternatives, ansible tower, Linux network devices. Programming with Python Python programming Functions. Classes, objects and instances, modules, libraries, packages. Python strings, Python file handling, pip list, pip instal. Hands on Python programming with pyping. More Python programming Functions. Classes, objects and instances, modules, libraries, packages. Python strings, Python file handling, pip list, pip install. Hands on Python programming with pyping. Git Distributed version control, repositories, Git and GitHub, Alternatives to GitHub, Installing git, git workflows, creating repositories, adding and editing files, branching and merging, merge conflicts. Hands on working with Git. Python and networking APIs, Sockets, Telnetlib, pysnmp, ncclient, ciscoconfparse. Paramiko SSH and Netmiko Integrating Python and network devices using SSH. Netmiko, Netmiko methods. Hands on Netmiko. NAPALM What is NAPALM, NAPALM operations, getters, Replace, merge, compare, commit, discard. Hands on Configuration with NAPALM. Integrating ansible and NAPALM. Python and REST REST APIs, enabling the REST API. Accessing the REST API with a browser, cURL, Python and REST, the request library. Hands on Using a REST API with network devices.

Network automation course description This course is not a soft skills course covering the concepts of DevOps but instead concentrates on the technical side of tools and languages for network DevOps. Particular technologies focussed on are ansible, git and Python enabling delegates to leave the course ready to starting automating their network. Hands on sessions follow all major sections. More detailed courses on individual aspects of this course are available. What will you learn Evaluate network automation tools. Automate tasks with ansible. Use git for version control. Use Python to manage network devices. Use Python libraries for network devices. Network automation course details Who will benefit: Network engineers. Prerequisites: TCP/IP foundation for engineers. Duration 5 days Network automation course contents What is DevOps Programming and automating networks, networks and clouds, AWS, OpenStack, SDN, DevOps for network operations. Initial configuration Configuring SSH, ZTP, POAP. Hands on Initial lab configuration. Getting started with ansible The language, the engine, the framework. Uses of ansible, orchestration. The architecture, Controlling machines, nodes, Agentless, SSH, modules. Configuration management, inventories, playbooks, modules, roles. Hands on Installing ansible, running ad hoc commands. Ansible playbooks ansible-playbook, YAML, plays, tasks, handlers, modules. Playbook variables. Register module, debug module. Hands on Running playbooks. Ansible Inventories /etc/ansible/hosts, hosts, groups, static inventories, dynamic inventories. Inventory variables, external variables. Limiting hosts. Hands on Static inventories, variables in inventory files. Ansible modules for networking Built in modules, custom modules, return values. Core modules for network operations. Cisco and/ or Juniper modules. ansible_connection. Ansible 2.6 CLI. Hands on Using modules. Ansible templating and roles Configuration management, full configurations, partial configurations. The template module, the assemble module, connection: local, Jinja2 templates, variables, if, for, roles. Hands on Generating multiple configurations from a template. Network programming and modules Why use Python? Why use ansible? alternatives, ansible tower, Linux network devices. Programming with Python Scripting versus application development, Python interactive mode, Python scripts, Python 2.7 vs Python 3. A simple Python script. Variables, loops, control statements, operators. PEP style guide. Python IDEs. Hands on Simple Python programs. More Python programming Functions. Classes, objects and instances, modules, libraries, packages. Python strings, Python file handling, pip list, pip install, Hands on Python programming with pyping. Git Distributed version control, repositories, Git and GitHub, Alternatives to GitHub, Installing git, git workflows, creating repositories, adding and editing files, branching and merging, merge conflicts. Hands on working with Git. Python and networking APIs, Sockets, Telnetlib, pysnmp, ncclient, ciscoconfparse. Paramiko SSH and Netmiko Integrating Python and network devices using SSH. Netmiko, Netmiko methods. Hands on Netmiko. PyEZ Juniper, NETCONF, installing PyEZ, a first pyEZ script, pyEZ configuration management. Hands on Juniper configuration management with pyEZ. NAPALM What is NAPALM, NAPALM operations, getters, Replace, merge, compare, commit, discard. Hands on Configuration with NAPALM. Integrating ansible and NAPALM. Python and REST REST APIs, enabling the REST API. Accessing the REST API with a browser, cURL, Python and REST, the request library. Hands on Using a REST API with network devices.

Network management technologies course description A comprehensive tour of the available network management technologies available for todays networks. The course starts with basic tools such as syslog along with Python network automation. SNMP is then covered with the *flow technologies and streaming telemetry. Configuration management with ansible, Python, NETCONF and RESTCONF is then studied. The final part of the course looks at SDN. Hands on sessions are used throughout to reinforce the theory rather than teach specific manufacturer equipment. Note that sections are available as individual courses. What will you learn Evaluate network management technologies. Evaluate network management technologies. Recognise the weaknesses of SNMP versus NETCONF and streaming telemetry. Explain the role of NETCONF and RESTCONF. Compare & contrast *flow and streaming telemetry. Explain the role of SDN in network management. Automate network configuration with ansible and Python. Network management technologies course details Who will benefit: Those wishing to manage networks. (Previous Python experience is NOT needed) Prerequisites: Intro to data comms Duration 5 days Network management technologies course content Basic network management Network management What is network management? Benefits, issues. FCAPS model. Fault management, Configuration management, accounting, performance, security. What to manage, what not to manage. Managing network devices, managing servers. Monitoring networks Traditional network tools Ping..., SSH, syslog, TFTP for configurations. nmap. Wireshark. CLI. Web based management. Splunk. Nessus, snort, Kali. Hands on syslog, network inventories. Network automation using the CLI Programming and automating networks, netOps. Python, Git. Python network modules, SSH, paramiko, netmiko. EVE-NG. Hands onPython network modules. Structured versus unstructured data Problems with automation and unstructured data. XML, JSON, YAML. The role of YANG. Hands on Parsing data. SNMP SNMP architecture, SNMP MIBs, SMI, the SNMP protocol, polling security. Configuring SNMP. SNMPv1, v2, v3, SNMP security. Which version should you use? MIBs and MIB structure. mib-2, extra parts of mib-2, Private enterprise MIBs. Summary: What SNMP is good/bad at. Hands on Configuring agents and a NMS. MIB browsing. Server management Microsoft, Linux, application polling. WMI vs SNMP. Hands on: Application polling. Performance management *flow Polling, push vs pull, netflow, sflow, IPFIX, *flow. Flows. Where to monitor traffic. Comparing *flow with SNMP. Architecture: Generators and collectors. When flows are exported. NetFlow reporting products. SolarWinds. Hands on Netflow configuration. Collectors. Streaming telemetry Model driven telemetry, periodic/on change. Structured data. Telemetry protocol stack. gRPC and gNMI. Protobuf. gNMI operations. Telemetry architecture. Telegraf, databases, Grafana. Hands on Telemetry example. Configuration management Configuration management tools Chef, puppet, ansible, saltstack. Ansible architecture, controlling machines, nodes, agentless, SSH, modules. Inventories, playbooks, modules, network modules, jinja2 templates. Hands on Network configuration with ansible. NETCONF What is NETCONF? Protocol stack, Data stores, traffic flows, validating configurations, rollback. YANG data models and how YANG is used by NETCONF. XML. Explorers and other tools. Hands on anx, Python and NETCONF. RESTCONF The REST API, HTTP, What is RESTCONF? Tools including Postman. Comparison with NETCONF. Hands on Configuration with RESTCONF. Python network automation: configuration SSH issues. Using structured data. Jinja2. ncclient, requests, NAPALM, Nornir. Automated testing. Hands on Python network device configuration with nornir. Software Defined Networks and orchestration Classic SDN What is SDN? benefits. SDN architecture. SDN applications, SDN switches, SDN controllers, Network Operating Systems. Control plane, data plane. Northbound interfaces. SDN components. Southbound interfaces. OpenFlow. ONF, OpenFlow ports, Flow tables. Network virtualization Virtual networks, virtual switches, NfV. Service chaining. NfV and SDN. SDN implementations Classic SDN, Hybrid SDN, SDN via APIs, SDN via overlays. Data centre SDN, VXLAN, Service Provider SDN, SD WAN, Enterprise SDN, WiFi. SDN and open source OpenDaylight, OpenVSwitch, Open Networking Forum, Open Network Operating System. Hands onOpenStack. SD-WAN What is SD-WAN? Architecture: Edge, gateway, orchestrator, controller. Overlay and underlay. Use of MPLS, 4G/5G. Benefits and features. Secure Access Service Edge (SASE).

OpenStack for NFV and SDN course description OpenStack is predominately a cloud management technology. This course looks at how OpenStack can be used in a NFV and SDN environment. What will you learn Describe the architecture of NFV. Explain the relationship between NFV and SDN. Implement NFV VIM using OpenStack. Explain how OpenStack as VNFM and orchestrator works. OpenStack for NFV and SDN course details Who will benefit: Anyone wishing to implement NFV using OpenStack. Prerequisites: Introduction to Virtualization Duration 3 day OpenStack for NFV and SDN course content What is NFV? What is NFV? What are network Functions? NFV benefits, NFV market drivers. ETSI NFV framework. ETSI documents, Architecture overview, compute domain, hypervisor domain, infrastructure network domain. What is OpenStack? Virtual machines, clouds, management. OpenStack architecture, OpenStack modules. Why OpenStack for NFV? Hands on OpenStack installation. OpenStack Virtualization and NFV Server, storage and network virtualization and NFV. Where OpenStack fits in the ETSI framework. Virtual machines, containers and docker. Data centres, clouds, SaaS, IaaS, PaaS. Hands on OpenStack Iaas, OpenStack Nova. The virtualization layer VM centric model, containers versus hypervisors, FD.io. Hands on OpenStack as the VIM. OpenStack Neutron VXLAN, Networks, subnets, ports. Security groups. Routers. Service and component hierarchy. Hands on Implementing a virtual network with OpenStack Neutron. Virtualization of Network Functions Network virtualization versus Network Function virtualization. NFV MANO Management and Orchestration. Where OpenStack fits. MANO descriptors, Open orchestration. OpenStack Tacker, Open MANO, OpenBaton, other orchestrators. OpenStack Tacker Installation, getting started, configuration. SFC and OpenStack. Hands on Deploying a VNF. OPNFV What is OPNFV, Where OpenStack fits into OPNFV. SDN What is SDN? Control and data planes. SDN controllers. Classic SDN versus real SDN. Hybrid SDN, network automation, SDN with overlays. Northbound, southbound, SDN protocols, OpenFlow, OpenDaylight, ONOS, SDN with NFV. SDN and OpenStack. Summary Deploying NFV, performance, testing. Futures

About this training course Artificial lift systems are an important part of production operations for the entire lifecycle of an asset. Often, oil and gas wells require artificial lift for most of the life cycle. This 5-day training course offers a thorough treatment of artificial lift techniques including design and operation for production optimization. With the increasing need to optimize dynamic production in highly constrained cost environments, opportunities and issues related to real-time measurements and optimization techniques needs to be discussed and understood. Artificial lift selection and life cycle analysis are covered. These concepts are discussed and reinforced using case studies, quizzing tools, and exercises with software. Participants solve examples and class problems throughout the course. Animations and videos reinforce the concepts under discussion. Understanding of these important production concepts is a must have to exploit the existing assets profitably. Unique Features: Hands-on usage of SNAP Software to solve gas-lift exercises Discussion on digital oil field Machine learning applications in gas-lift optimization Training Objectives After the completion of this training course, participants will be able to: Understand the basics and advanced concepts of each form of artificial lift systems including application envelope, relative strengths, and weaknesses Easily recognize the different components from downhole to the surface and their basic structural and operational features Design and analyze different components using appropriate software tools Understand challenges facing artificial lift applications and the mitigation of these challenges during selection, design, and operation Learn about the role of digital oilfield tools and techniques and their applications in artificial lift and production optimization Learn about use cases of Machine learning and artificial intelligence in the artificial lift Target Audience This training course is suitable and will greatly benefit the following specific groups: Production, reservoir, completion, drilling and facilities engineers, analysts, and operators Anyone interested in learning about selection, design, analysis and optimum operation of artificial lift and related production systems will benefit from this course. Course Level Intermediate Advanced Training Methods The training instructor relies on a highly interactive training method to enhance the learning process. This method ensures that all participants gain a complete understanding of all the topics covered. The training environment is highly stimulating, challenging, and effective because the participants will learn by case studies which will allow them to apply the material taught in their own organization. Course Duration: 5 days in total (35 hours). Training Schedule 0830 - Registration 0900 - Start of training 1030 - Morning Break 1045 - Training recommences 1230 - Lunch Break 1330 - Training recommences 1515 - Evening break 1530 - Training recommences 1700 - End of Training The maximum number of participants allowed for this training course is 20. This course is also available through our Virtual Instructor Led Training (VILT) format. Prerequisites: Understanding of petroleum production concepts. Each participant needs a laptop/PC for solving class examples using software to be provided during class. Laptop/PC needs to have a current Windows operating system and at least 500 MB free disk space. Participants should have administrator rights to install software. Trainer Your expert course leader has over 35 years' work-experience in multiphase flow, artificial lift, real-time production optimization and software development/management. His current work is focused on a variety of use cases like failure prediction, virtual flow rate determination, wellhead integrity surveillance, corrosion, equipment maintenance, DTS/DAS interpretation. He has worked for national oil companies, majors, independents, and service providers globally. He has multiple patents and has delivered a multitude of industry presentations. Twice selected as an SPE distinguished lecturer, he also volunteers on SPE committees. He holds a Bachelor's and Master's in chemical engineering from the Gujarat University and IIT-Kanpur, India; and a Ph.D. in Petroleum Engineering from the University of Tulsa, USA. Highlighted Work Experience: At Weatherford, consulted with clients as well as directed teams on digital oilfield solutions including LOWIS - a solution that was underneath the production operations of Chevron and Occidental Petroleum across the globe. Worked with and consulted on equipment's like field controllers, VSDs, downhole permanent gauges, multiphase flow meters, fibre optics-based measurements. Shepherded an enterprise-class solution that is being deployed at a major oil and gas producer for production management including artificial lift optimization using real time data and deep-learning data analytics. Developed a workshop on digital oilfield approaches for production engineers. Patents: Principal inventor: 'Smarter Slug Flow Conditioning and Control' Co-inventor: 'Technique for Production Enhancement with Downhole Monitoring of Artificially Lifted Wells' Co-inventor: 'Wellbore real-time monitoring and analysis of fracture contribution' Worldwide Experience in Training / Seminar / Workshop Deliveries: Besides delivering several SPE webinars, ALRDC and SPE trainings globally, he has taught artificial lift at Texas Tech, Missouri S&T, Louisiana State, U of Southern California, and U of Houston. He has conducted seminars, bespoke trainings / workshops globally for practicing professionals: Companies: Basra Oil Company, ConocoPhillips, Chevron, EcoPetrol, Equinor, KOC, ONGC, LukOil, PDO, PDVSA, PEMEX, Petronas, Repsol, , Saudi Aramco, Shell, Sonatrech, QP, Tatneft, YPF, and others. Countries: USA, Algeria, Argentina, Bahrain, Brazil, Canada, China, Croatia, Congo, Ghana, India, Indonesia, Iraq, Kazakhstan, Kenya, Kuwait, Libya, Malaysia, Oman, Mexico, Norway, Qatar, Romania, Russia, Serbia, Saudi Arabia, S Korea, Tanzania, Thailand, Tunisia, Turkmenistan, UAE, Ukraine, Uzbekistan, Venezuela. Virtual training provided for PetroEdge, ALRDC, School of Mines, Repsol, UEP-Pakistan, and others since pandemic. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations

Ansible for engineers training course description An introduction to automation using ansible. Ansible is a general purpose IT automation platform that can be use for a number of purposes. The course covers configuration management, cloud provisioning and application deployment with ansible. Hands on sessions follow all major sections. What will you learn Install ansible. Automate tasks with ansible. Write ansible playbooks. Ansible for engineers training course details Who will benefit: Administrators and developers automating tasks. Prerequisites: Linux administration skills Duration 3 days Ansible for engineers training course contents What is ansible? The language, the engine, the framework. Uses of ansible, orchestration. Hands on Installing ansible. Ansible architecture ible architecture Controlling machines, nodes, Agentless, SSH, modules, JSON protocol. Configuration management, inventories, playbooks, modules, roles. Hands on Getting started, running ad hoc commands. Ansible and Vagrant Prototyping and testing. Hands on Using ansible with Vagrant. Ad hoc commands Parallelism, shell commands, managing files and directories, file transfer, package management, manage user and groups, deploying applications, service management, background jobs, checking log files, managing cron jobs. Hands on Using ansible with Vagrant. Playbooks ansible-playbook, users, sudo, YAML, plays, tasks, handlers, modules. Hands on Running playbooks. More playbooks Handlers, variables, environmental variables, playbook variables, inventory variables, variable scope and precedence, accessing variables, facts, ansible vault. Conditionals, wait_for. Hands on Using variables and conditions in playbooks. Roles and includes Dynamic includes, Handler includes, playbook includes. Roles, role parts: handlers, files, templates, cross platform roles, ansible galaxy. Hands on includes example, building roles. Inventories /etc/ansible/hosts, inventory variables, static inventories, dynamic inventories. Hands on Inventories and variables. Miscellanea Individual server cookbooks, Main playbook for configuring all servers. Hands onPlaybooks.

About this Training Course There are various kinds of geophysical data available. They are separated into seismic and non-seismic (multi-physics) data. Non-seismic or multi-physics data (which includes gravity, magnetics, electrical, electromagnetics, spectral etc - apart from providing complimentary information to seismic) is the main source of information for very shallow subsurface applications such as engineering, mapping pollution, archaeology, geothermal energy, and related areas. This 5 full-day blended course will focus specifically on seismic data which is the main method used in the Oil & Gas industry. In this blended course, participants will be equipped to understand that seismic data represents the movement of the surface, resulting from waves generated by a source, dynamite or vibrator which are reflected by changes in the subsurface rocks. The basic principles of acquisition and processing will be explained and insights into advanced methods, allowing a much more accurate interpretation of seismic data than previously considered possible, will also be provided. This blended course contains an introduction to Machine Learning and its important role in all aspects of seismic acquisition, processing, and interpretation. There is no need to know in detail how the algorithms work internally but it is necessary to know how to use them correctly to achieve optimum results. Training Objectives By attending this course, participants will be able to acquire the following: Obtain an understanding of the strengths and limitations of geophysical methods, specifically seismic, and the costs and risks involved, and how to reduce these. Be able to communicate more effectively with staff in other disciplines. Understand the potential applications of seismic data and know how to formulate the requirements needed for prospect and field evaluation. Gain an awareness of modern seismic technology. Apply the learning in a series of practical, illustrative exercises. Know what types of questions to ask to assess the necessary quality of a seismic project in its role in a sequence of E&P activities Target Audience The blended course is intended for non-geophysicists who have intensive interaction with geophysicists. But it may be of interest to those who want to know about the recent progress made in geophysics, leading to amazing imaging results, which could not be imagined a decade ago. The blended course will bring to the attention of the geologists, petrophysicists and reservoir/petroleum engineers an awareness of how the data they will work with is acquired and processed by the geophysicist. It will introduce the concepts that are of importance in geophysics and thus relevant for non-geophysicists to know and be able to communicate with geophysicists as well as formulate their requests. Course Level Intermediate Trainer Your expert course leader has degree in Geology (University of Leiden), a Master's degree in Theoretical Geophysics (University of Utrecht) and a PhD in Utrecht on 'Full wave theory and the structure of the lower mantle'. This involved forward modelling of P- and S-waves diffracted around the core-mantle boundary and comparison of the frequency-dependent attenuation of the signal with those obtained from major earthquakes observed at long offsets in the 'shadow zone' of the core. These observations were then translated into rock properties of the D' transition zone. After his PhD, he joined Shell Research in The Netherlands to develop methods to predict lithology and pore-fluid based on seismic, petrophysical and geological data. He subsequently worked for Shell in London to interpret seismic data from the Central North Sea Graben. As part of the Quantitative Interpretation assignment, he was also actively involved in managing, processing and interpreting Offshore Seismic Profiling experiments. After his return to The Netherlands, he headed a team for the development of 3D interpretation methods using multi-attribute statistical and pattern recognition analysis on workstations. After a period of Quality Assurance of 'Contractor' software for seismic processing, he became responsible for Geophysics in the Shell Learning Centre. During that period, he was also a part-time professor in Applied Geophysics at the University of Utrecht. From 2001 to 2005, he worked on the development of Potential Field Methods (Gravity, Magnetics) for detecting oil and gas. Finally, he became a champion on the use of EM methods and became involved in designing acquisition, processing and interpretation methods for Marine Controlled Source EM (CSEM) methods. After his retirement from Shell, he founded his own company, specialising in courses on acquisition, processing and interpretation of geophysical data (seismic, gravity, magnetic and electromagnetic data), providing courses to International and National energy companies. In the last couple of years, he became keenly interested in the use of Machine Learning in Geophysics. Apart from incorporating 'Artificial Intelligence' in his courses, he also developed a dedicated Machine Learning course for geophysics. POST TRAINING COACHING SUPPORT (OPTIONAL) To further optimise your learning experience from our courses, we also offer individualized 'One to One' coaching support for 2 hours post training. We can help improve your competence in your chosen area of interest, based on your learning needs and available hours. This is a great opportunity to improve your capability and confidence in a particular area of expertise. It will be delivered over a secure video conference call by one of our senior trainers. They will work with you to create a tailor-made coaching program that will help you achieve your goals faster. Request for further information post training support and fees applicable Accreditions And Affliations