- Prepare and teach two weekly 90-minute labs, for 12 weeks, in person or online, depending on course needs;
- Attend and support one of the weekly Deep Learning lecture;
- Student support, of which at least 60 weekly minutes should be regular office hours;
- Provide grading support and proctoring of exams and assignments for the instructor and assist with the development of the assignments themselves;
- For Lead TAs: Exam wrap-up and additional tasks of the Lead TA.
- A strong understanding of machine learning and especially deep learning (students at the Master‐ or PhD‐level, or equivalent practical experience).
- Experience with common deep learning frameworks such as TensorFlow or PyTorch.
- Excellent pedagogical skills (you should be able to explain the key concepts to highly motivated and hard‐working students with relatively little background in machine learning); an interest in developing teaching materials
- An interest in machine learning applications in public policy, as well as the governance of AI, is a plus.
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Lead Teaching Assistant - Berlin, Deutschland - Escuela Hertie
Beschreibung
The Hertie School in Berlin prepares
exceptional students for leadership positions in government, business, and civil society. The school offers master's programmes, executive education and doctoral programmes, distinguished by interdisciplinary and practice-oriented teaching, as well as outstanding research. Its extensive international network positions it as an ambassador of good governance, characterised by public debate and engagement. The school was founded in 2003 by the Hertie Foundation, which remains its major funder. The Hertie School is accredited by the state and the German Science Council. We look to hire one Teaching Assistant (TA) for Prof. Dr. Lynn Kaack's course Deep Learning in the Master of Data Science for Public Policy (MDS) of the Hertie School, for the Fall semester August 2024 to 31 January 2025).
As the fastest growing subfield of machine learning, deep learning is the technology behind facial recognition, machine translation, ChatGPT, and many other well-known applications. As policy makers are beginning to regulate machine learning, technical understanding of deep learning in public policy is invaluable. In policy research and analysis, deep learning has only recently started to be applied. In this course, students will learn the main theoretical concepts of (deep) neural networks, and get introduced to applications in computer vision, natural language processing and other areas. Students will gain hands-on experience by training and testing their own models in policy-relevant applications. The main objective of this course is to enable students to scope out new meaningful and robust deep learning applications, and to advise decision makers on strengths and limitations of the technology.
Your tasks:
Your profile:
We offer:
A stimulating international and diverse environment in multiple areas of social science, high-quality teaching and public policy. The Hertie School is a vibrant academic community that emphasizes excellence in research and teaching as well as an interdisciplinary perspective. Our school has been certified as a family friendly work environment in higher education and an equal opportunity employer. Severely disabled applicants are given preferential consideration in the event of equal qualification.
The total workload is 195 hours (average of 7.5 hours per week) over the 6-month contract (01 August 2024 to 31 January 2025) or 9 hours per week over a 5-month contract (1 September 2024 to 31 January The actual weekly workload can vary over the semester and will be agreed upon with the supervisor.
Please submit a CV and a short cover letter via the button below. We review applications on a rolling basis until the position is filled.
Please contact Lynn Kaack, Assistant Professor of Computer Science and Public Policy, for more questions about the position.
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