Intern in the Electrical Department

Our team and mission

The Electrical Department is responsible for executing activities in the following domains: electromagnetics, antenna systems, space environment, power system, data handling for payloads and platforms, computers and microelectronics, communications and navigation payloads and end-to-end systems. The Department manages the corresponding technical laboratories and facilities, elaborates Engineering Standards and is responsible for technology activities for the technical discipline areas of the Department.

The On-Board Computer & Data Handling Section provides functional support to ESA projects and carries out technological research (R&D) what concerns turn-key on-board HW Data Handling solutions with emphasis on: 
-    platform and payload data handling architectures and their building blocks (equipment/units, modules and key components);
-    units such as on-board computers, mass memories, remote terminals, instrument control units*;
-    digital and analogue signal processing electronics for payload/platform functions;
-    front-end acquisition and processing chain electronics*;
-    on-board data transfer interfaces, buses and associated protocols (high and low speed);
-    platform data handling functions related to security, data authentication, encryption, compression;
-    use of microelectronics devices.
-    implementation, inference, verification and validation of algorithms** on processing HW platforms for space applications* in close collaboration with other discipline experts (software, microelectronics and applications engineers).

* except for RF payloads.
** including Artificial Intelligence and Machine Learning algorithms.

For further information visit our web site: http://www.esa.int

Field(s) of activity for the internship

You can choose between the following topics:

1) Topic 1: Reliable AI for On-Board Processing

With the increased number and complexity of vision based payloads, the on-board data rates are ever-increasing. The amount of data generated is becoming too great to be able to all be sent back on the ground. Therefore, advanced data processing concepts for reducing the amount of data are needed. For vision based payloads one of the promising options is to use AI inference to filter some of the data. The harsh space environment places a lot of restrictions for the type of hardware that can reliably be used on-board. The offered internship will allow you to work on space-grade hardware to implement various strategies to increase the reliability of AI inference. The internship will include deploying existing neural networks onto HW, analyzing the cost-benefits of different mitigation schemes (quantization, redundancy, fault aware training etc.), error injection and FPGA implementation of the neural networks.

2) Topic 2: Payload data handling and storage

Volume and data rates of scientific on-board payload data will increase in the following years drastically and therefore new concepts and technologies are required for processing, transferring and storing the data. The offered internship will allow you to work on a specific topic in this domain, which can e.g. be related to high-speed serial links and associated protocols, FPGA designs for mass memory units (MMUs), flash memory device selection for MMUs, or the implementation of MMU-internal processing functions (compression, cryptography, file delivery protocols). This training opportunity will provide hands-on hardware and software development experience.

Behavioural competencies

Result Orientation
Operational Efficiency
Fostering Cooperation
Relationship Management
Continuous Improvement
Forward Thinking

Education

You must have student status and be enrolled at university for the entire duration of the internship. You should preferably be in your final or second to last year of a university course at master’s level in a technical or scientific discipline.

Additional requirements

The working languages of the Agency are English and French. A good knowledge of one of these is required. Knowledge of another Member State language would be an asset.

1) Topic 1:
Microelectronics background, including digital integrated circuit modelling languages (e.g. VHDL or Verilog) and computer aided design tools for digital design from e.g. Synopsys, Mentor and Cadence. Python knowledge and experience in Tensorflow/Pytorch/other AI framework. Basic knowledge of Docker and gitlab.

2) Topic 2: 
Microelectronics background, including digital integrated circuit modelling languages (e.g. VHDL, Verilog, SystemVerilog and SystemC) and computer aided design tools for digital design from e.g. Synopsys, Mentor and Cadence.