Machine Learning (Algorithm Development)

Our clients algorithms are at the heart of designing advanced guided systems. They are developed throughout the product lifecycle, from initial research to future advancements.

Intelligent Autonomous Systems (IAS) Engineers contribute at every stage of project development, playing a key role in the following areas:

Technical development of specific algorithms or studies for major programs
Feasibility studies, algorithm design, trade-off analysis, trial preparation, trial analysis and reporting, defining architecture, and validating algorithms and models
Conducting technical assessments and investigations into a wide range of issues and developing solutions individually or as part of a team
Collaborating with algorithm users to understand and address their needs, ensuring algorithms are fit for purpose.What we're looking for from you:

A degree or PhD in a related field, or a degree with a strong mathematical foundation and programming skills.
Relevant experience (Post-Doctoral or industrial) in robotics, data fusion, tracking/estimation, pattern recognition, statistical inference, optimization, and machine/deep learning algorithms, along with real-time implementation, validation, and verification.
Proficiency in tools like Matlab, Simulink, Stateflow, Python (including PyTorch, TensorFlow, Open AI-Gym/Universe), or Model-Based Design is desirable.We encourage clients IAS Engineers to develop broad, in-depth knowledge across a variety of fields. Specific knowledge or experience in the following areas would be an advantage:

Robotics, guidance, and autonomous decision-making, such as routing and motion/trajectory planning, optimization, coordinated guidance and control, decision theory, MDPs/POMDPs, specialist systems, game theory, decision support systems, and multi-agent systems.
Data fusion and state estimation/tracking algorithms like Kalman Filtering, multiple-model tracking methods, particle filters, grid-based estimation, Multi-Object-Multi-Sensor Fusion, data association, random finite sets, Bayesian belief networks, and Dempster-Shafer theory of evidence.
Machine Learning for regression, pattern recognition, and discovery, including Gaussian processes, latent variable methods, support vector machines, probabilistic/statistical models, neural networks, Bayesian inference, random forests, novelty detection, and clustering.
Deep Learning such as Deep reinforcement learning, Monte-Carlo tree search, deep regression/classification, deep embeddings, recurrent networks, and natural language processing.
Computer Vision algorithms, including structure from motion, image-based navigation, SLAM, and pose estimation/recovery.

For more detail on this great opportunity, please reach out to me directly