PhD Visual Analytics for 3DOMICS
Faculty Electrical Engineering, Mathematics and Computer Science
Level: University Graduate
Working hours: 38-40 hours weekly
Contract: 4 years
Salary: 2325 - 2972 euros monthly (full-time basis)
Faculty Electrical Engineering, Mathematics and Computer Science
The Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) is known worldwide for its high academic quality and the social relevance of its research programmes. The faculty’s excellent facilities accentuate its international position in teaching and research. Within this interdisciplinary and international setting the faculty employs more than 1100 employees, including about 400 graduate students and about 2100 students. Together they work on a broad range of technical innovations in the fields of sustainable energy, telecommunications, microelectronics, embedded systems, computer and software engineering, interactive multimedia and applied mathematics.
The Department of Intelligent Systems (INSY) conceptualises computer science methodologies to sense, abstract, learn, reason, elicit and adapt data and their meaning in ways that respect human values in order to increase human effectiveness, well-being and social innovation. At the heart of the department is therefore research and teaching in computer science theory, algorithms and solutions for information processing systems that support humans (e.g. robotics), new products (e.g. Internet services), and science (e.g. biology).
The project will be carried out in close collaboration with biologists and computational researchers at LUMC. LUMC is one of the pioneering institutions, applying Conventional and Imaging Mass Cytometry to diverse problems in health research in Europe. During previous collaborations, we have developed robust methods and applied them in high impact bio-/medical studies.
The Computer Graphics and Visualisation Group has a strong research record and is known for its expertise in visual analytics, visualisation in general, modelling, game technology, and rendering. Besides these major topics, interaction techniques, virtual/augmented reality, vision, perception, computational photography, and simulations play an important role. The group also participates strongly in the Delft Research Initiatives “Environment” and “Health”, thus aiming to find solutions for tomorrow’s problems. It maintains a strong network of partners worldwide from academia, medical centres and industry.
There is an urgent need for fast and scalable visual analytics methods to capitalize on the discovery potential of multimodal, high-dimensional data. This position aims to develop methods to support the interactive exploration of such data for hypothesis generation which is the main analysis bottleneck. These methods developed will be inspired by the needs of single cell and spatially resolved imaging "-omics" (3DOMICS) data. Without further advances towards scalable, interpretable pattern discovery methods, much of the potential of large-scale single-cell / 3DOMICS experiments will remain buried in the data.
The rapid advancements in single-cell measurement technologies are currently revolutionizing biology, with clinical impact from immunology to cancer research to neuroscience. 3DOMICS techonologies allow the acquisition of tens to hundreds of genomics and/or proteomics measurements at subcellular resolution. For example, in Imaging Mass Cytometry a single pixel represents measurments of up to 50 different proteins over an area of 1μm squared. This allows for the extraction of cellular structures and their functionality and can show how cells are organized in their natural “tissue habitat”. Such data holds the key to unraveling diverse disease mechanisms, from interaction between immune and cancer cells to how the immune system derails in auto-immune diseases. However, the amount and complexity of the data mandate fast, scalable and interpretable data analytics methods. The aim of this project is to develop visual analytics techniques for integrated exploration of cellular contexts from multi-modal, large scale single cell- and 3DOMICS data.
We have recently demonstrated that hierarchical dimensionality reduction strategies such as Hierarchical Stochastic Neighbor Embedding (HSNE) represent a major step towards effective analysis of single-cell data at scale: enabling an interactive interpretation of data sets, while preserving the details of the data. We aim towards combining massive single-cell and spatially resolved data sets through this visual analytics concept. The sheer data size and complexity of a single experiment is becoming the most important bottleneck towards understanding the patterns in the data. Also, visualization and effective expert interaction with such large datasets is essential for hypothesis generation.
This position is part of a larger project focused in immunology and neuroscience. In collaboration with various research partners including Leiden University Medical Center (LUMC), and with the Allen Brain Institute in Seattle.
We are looking for a candidate who meets the following requirements:
• You are a talented and enthusiastic researcher.
• You have experience with or a strong background in visual analytics or visualization, computer graphics, and pattern recognition. Preferably you finished a master in Computer Science, (Applied) Mathematics, (Applied) Physics or Electrical Engineering.
• You have excellent programming skills and experience.
• You have good communication skills, and the abilty to participate successfully in the work of a multidisciplinary research team.
• You are creative, ambitious, hardworking and persistent.
• You have a good command of the English language (knowledge of Dutch is not required).
Conditions of employment
TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.tudelft.nl/phd for more information.
Information and application
For more information about this vacancy please contact Anna Vilanova, email: email@example.com , tel: +31 152783107.
To apply, please e-mail a detailed CV (with contact to two referees) along with a letter of motivation and a detailed transcript of university grades. If applicable, please also attach a (draft) version of your Master thesis. Please e-mail your application before July 20, 2019 to Hrfirstname.lastname@example.org mentioning vacancy number EWI2019-36.
Enquiries from agencies are not appreciated.
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