1-2 Postdoctor (2 years) within Optimization for Machine Learning
Found in: Talent SE C2 - 2 weeks ago
Project description
and working tasksThe connection between optimization and machine learning is at the heart of many recent breakthroughs in artificial intelligence and autonomous systems. Many machine learning applications are modeled as optimization problems, and as problems become more and more complex, new optimization methods are needed. Within this framework, the successful candidate is encouraged to develop their research agenda in close collaboration with their supervisor. Potential areas of interest include, but are not limited to: Efficient and scalable algorithms for machine learning, Optimization problems with stochastic constraints, Implicit regularization in neural networks, Optimization with quantum computation, Distributed optimization and federated learning, Operator splitting methods in optimization, Continual learning, Bilevel optimization and applications in machine learning, Representation learning, Optimization on manifolds.
Qualifications
To be appointed under the postdoctoral agreement, the postdoctoral fellow is required to have completed a doctoral degree or a foreign degree deemed equivalent to a doctoral degree. These qualification requirements must be fulfilled no later than at the time of the appointment decision.
To be appointed under the postdoctoral agreement, priority should be given to candidates who completed their doctoral degree, according to what is stipulated in the paragraph above, no later than three years prior. If there are special reasons, candidates who completed their doctoral degree prior to that may also be eligible. Special reasons include absence due to illness, parental leave, appointments of trust in trade union organisations, military service, or similar circumstances, as well as clinical practice or other forms of appointment/assignment relevant to the subject area. Postdoctoral fellows who are to teach or supervise must have taken relevant courses in teaching and learning in higher education.
Documented knowledge and proven experience in optimization or machine learning are required. In addition, good programming skills (preferably MatLab or Python) and communication skills in written and spoken English are required. A solid track record of publication and experience in optimization for machine learning are strong merits. The research tasks require independence, accuracy, and dedication.
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