Ongeveer 23 uur geleden - Technische Universiteit Delft (TUD) - Delft
PhD position Distributed Systems
Machine learning has become the core of many of today’s applications, such as self-driving cars and detecting security threats. Due to the large data volume …
- Mekelweg, Delft, Zuid-Holland
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 38 - 40 uur
- € 2395 - € 3061 per maand
Machine learning has become the core of many of today’s applications, such as self-driving cars and detecting security threats. Due to the large data volume and ubiquitous data sensing from edge devices, e.g., smart phones, machine learning is shifting from the centralized cloud mode to distributed systems where edge nodes can train and update models (a)synchronously. The research objective of this PhD project is to derive scalable distributed algorithms for (deep) machine learning models. The key tasks of this project are: (i) analysing the performance of a wide range of distributed machine learning algorithms on different platformss (ii) exploring hybrid computing models between edge nodes and the cloud, (iii) designing algorithms to optimize the tradeoff between computing and communication of distributed learners, and (iv) designing resource management policies for real-time executions of applications.
The Department of Software Technology (ST) is one of the leading Dutch departments in research and academic education in computer science, employing over 150 people. The department ST is responsible for a large part of the curriculum of the bachelor’s and master’s programmes in Computer Science as well as the master’s programme in Embedded Systems. The inspiration for its research topics is largely derived from technical ICT problems in industry and society related to large-scale distributed processing, embedded systems, programming productivity, and web-based information analysis.
The Distributed Systems group (www.ds.ewi.tudelft.nl), under the leadership of Prof. Dick Epema, performs world-class research in the design, implementation, deployment, and analysis of large-scale, Internet-based computer systems. It currently has three research lines: scheduling and resource management in distributed computing systems (e.g., in clusters and clouds), big-data analytics (e.g., differential approximate processing), and cooperative systems (blockchain technology, trust and reputation systems). Its research is fundamental, aimed at the development and evaluation of new generic concepts in systems software, and application-driven, motivated by important application areas. Much of it is experimental, validating the proposed new concepts by means of implementation and deployment in prototypes that are used in the real world.
We are looking for candidates who satisfy the following requirements:
- an MSc degree with excellent results in Computer Science and Mathematics, preferably in distributed systems, theory, or related areas.
- experience in writing python and scala code and conducting scientific evaluations through experimentation.
- solid knowledge in (deep) machine learning algorithms.
- good speaking and writing skills in English.
TU Delft offers PhD-candidates a 4-year contract, with an official go/no go progress assessment after one year. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities, increasing from € 2325 per month in the first year to € 2972 in the fourth year. As a PhD candidate you will be enrolled in the TU Delft Graduate School. The TU Delft Graduate School provides an inspiring research environment with an excellent team of supervisors, academic staff and a mentor. The Doctoral Education Programme is aimed at developing your transferable, discipline-related and research skills.
The TU Delft offers a customisable compensation package, discounts on health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. For international applicants we offer the Coming to Delft Service and Partner Career Advice to assist you with your relocation.
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