30 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"https:" positions at Leibniz
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able to take the initiative willingness to integrate into an international working environment willingness to attend business trips, therefore a Class B driver's license (car) is an advantage We offer
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(FIJI/ImageJ etc.) Ability to work independently and to supervise interns and undergraduates Excellent English (in speaking and writing) and computer skills, knowledge of German is a bonus, but not
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, covering the involved actors and political dynamics on both sides, escalation risks and the military balance of power Collaborating on the development of conflict scenarios for an AI-supported scenario
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public transport or car (including free parking) 30 days of vacation Participation in the benefits program for employees („Corporate Benefits“) The advertised position is available for 3 years. We look
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using the public transport Easy accessibility by public transport or car (including free parking) 30 days of vacation Participation in the benefits program for employees („Corporate Benefits
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. Excellent English language skills for both oral and written. communication; learning German for stakeholder interactions is expected. Enthusiasm to collaborate in a team of aquatic scientists from diverse
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machine learning algorithms Strong communication skills and ability to work in interdisciplinary teams Fluency in spoken and written English We offer: A dynamic and interactive research environment as
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The Leibniz Institute for Neurobiology (LIN) is an internationally recognized neuroscientific research institute and dedicated to the research on learning and memory. Our research comprises all
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production for additive manufacturing, to the analysis of the final machined components, for example high-precision gears. This is how we shape the requirements of tomorrow. What you can expect from us
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of self-motivation to achieve a doctorate Analytical, creative and solution-oriented mindset Strong interest in familiarising yourself with new topics and learning complementary biophysical techniques