Scientific Programmer in Machine Learning (80-100%)

The Lucerne School of Information Technology is the first school in a Swiss university of applied sciences to be devoted to IT. The new Zug-Rotkreuz campus offers bachelor degree programmes in Digital Ideation, Computer Science, Information and Cyber Security, International IT Management and Business Information Technology and master’s degree programmes in Engineering and Business Information Technology. Rounding out the School’s offer are attractive continuing education courses and applied research and services. It is one of six Schools making up the Lucerne University of Applied Sciences and Arts with its 1600-plus staff and 10,800 or so degree and continuing education students.

We are looking for an individual to join our team with immediate effect or by mutual agreement as a

Scientific Programmer in Machine Learning (80-100%)

• You work jointly with our lecturers and academic staff on research and development projects with a focus on machine learning.
• You engage in project implementation, take responsibility for work-packages in research projects and independently investigate research questions by developing implementation concepts and solutions for our industrial partners.
• You support our research team in the acquisition of new research projects and in the supervision of doctoral students in cooperation with our university partners.
• You supervise student projects and diploma theses. Participation in lecturing or further education is possible according to agreement.

• PhD in Computer Science or Mathematics
• Experience in implementation of machine learning projects
• Knowledge in computer vision or natural language processing is an advantage
• Solid analytical and conceptual skills
• Ambition to bring innovative systems to industrial maturity; this requires good knowledge in software development and in dealing with machine learning technologies like Python (scikit-learn), TensorFlow or Keras
• Fluent in German and English and enjoy working in a success-oriented and interdisciplinary team
• Active contribution in our self-organizing research team