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application! We are looking for a PhD student in Statistics and Machine Learning Your work assignments We are looking for a PhD candidate to work in the intersection of computational statistics and machine
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. This is a part-time, 50% FTE position. Additional FTE (up to 100%) may be available based on evolving program needs Location: Seattle or hybrid, with statewide travel to partner sites, as needed. Project
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physics (mandatory) Good didactic skills (mandatory) High written and oral expression skills (mandatory) Computer user skills (mandatory) Excellent command of English (mandatory) Ability to work in a team
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for a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1
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engineering Engineering » Computer engineering Engineering » Control engineering Engineering » Materials engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline
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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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2026 - 12:16 (UTC) Country Ireland Type of Contract Temporary Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff
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RPTU University Kaiserslautern-Landau • | Kaiserslautern, Rheinland Pfalz | Germany | about 20 hours ago
Degree PhD Course location Kaiserslautern Teaching language English Languages Discussions are held in English (100%). Programme duration 8 semesters Beginning Only for doctoral programmes: any time
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at scientific symposia. Excellent knowledge of the English language. Advanced computer skills. Probation period: 4 months. Applicants for the post of senior assistant ought to have been engaged in scientific
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this position, you will ideally bring the following: For the Networking and AI position: Completed a PhD in Computer Science, Electrical & Computer Engineering, or a closely related field Expert knowledge of deep