7 of 7 Remote/Hybrid Permanent Applied Mathematics Jobs

depth, judgment, and evidence of exceptional work than credentials. You may have PhD-level training in machine learning, physics, biology, chemistry, applied mathematics, computational biology, or another systems-oriented discipline. You should have strong foundations in one or more of: applied mathematics, statistics, optimisation … probabilistic modelling, causal inference, dynamical systems, scientific ML, or related areas. You may also be an exceptional applied ML engineer without a PhD, with a track record of building models for complex real-world systems. Experience with biological data is useful, but not required. What matters most ...

Comfortable being the software person in a hardware company (substantial work is geometric and physical modelling) Nice to have: A degree in Computer Science, Mathematics, Physics, Mechanical Engineering, or Computational Engineering A Masters or PhD in computational geometry, applied mathematics, continuum mechanics, computer graphics … related discipline Self-taught engineers with a strong shipping record are equally valid Differential geometry, optimisation, or applied ML background Medical device software experience ...

offs, and recommendations clearly to technical and non-technical audiences. Requirements Experience in data science, machine learning, artificial intelligence, NLP, statistics, applied mathematics, computer science, or a related quantitative area. Experience working with frontier LLMs such as OpenAI’s GPTs, Anthropic’s Claude, and Google’s Gemini ...

large and complex datasets, and collaborating closely with data engineers and data scientists to deliver robust analytical products. Advanced knowledge of applied mathematics, statistics, and numerical analysis, with experience using techniques such as optimisation, time series analysis, simulation or predictive modelling to generate insight and support decision ...

large and complex datasets, and collaborating closely with data engineers and data scientists to deliver robust analytical products. Advanced knowledge of applied mathematics, statistics, and numerical analysis, with experience using techniques such as optimisation, time series analysis, simulation or predictive modelling to generate insight and support decision ...

collaborating closely with data engineers and data scientists to deliver robust analytical products. Skills/Knowledge Essential Advanced knowledge of applied mathematics, statistics, and numerical analysis, with experience using techniques such as optimisation, time series analysis, simulation or predictive modelling to generate insight and support decision making. ...

variety of engineering disciplines: control theory, software development, signal/video processing, pattern recognition, machine learning, artificial intelligence, high performance computing, applied mathematics, network/web services, and visualization. Support prototype evaluation, experimental operations at military facilities, and transitions of capabilities to government. Support system integration events ...