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PhD Positions at the Max Planck Graduate Center for Quantum Materials (# of pos: 12)

Quantum Materials

The term “quantum materials” is shorthand for a rapidly evolving research frontier that aims to understand, control, and ultimately design materials in which quantum physics enables novel functionalities. Recent developments include the discovery of novel phenomena resulting from the topology of the electron wave function in solids, the dynamical control of many-electron states (such as superconductivity) by coherent light fields, and the synthesis of heterostructures and devices comprising atomically sharp interfaces between different quantum materials. Six Max Planck Institutes in Germany (Berlin, Dresden, Hamburg, Halle, and Stuttgart) participating in the MP Graduate Center for Quantum Materials. All are world-leading institutions in their respective fields, and they operate highly attractive Ph.D. programs in cooperation with universities both in Germany and abroad.

Our offer

We offer our students a truly unique research environment with access to a large number of world-class research facilities, as well as a newly designed curriculum covering the frontiers of research on quantum materials. As a key new feature, the Center will introduce a set of high-level “Frontiers of Research” courses closely aligned with research at the Max Planck Institutes. Examples of “Frontiers of Research” in quantum materials are superconductivity, synthesis of quantum materials, quantum heterostructures, spin orbitronics, neuromorphic computing, or dynamical control. These courses will typically taught to small groups of students. In order to offset the geographical separation of the participating institutions, students from different locations will gather for courses at a central location. Beside these block course, students can benefit from a dedicated network of “immersive” video studios at all institutes involved in the Graduate Center. The “Frontiers in Research” series and other courses will be complemented by a number of courses which are offered at the participating MPIs and the associated International Max Planck Research Schools (IMPRS). We offer an English-language curriculum comprising lecture courses, a training program in complementary skills, and summer and winter schools with leading international partner institutions. This will provide you a unique opportunity to kick-start a successful scientific career. All accepted young researcher will receive financial support in the form of stipends or working contracts according to the German public sector.

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Europe

6 Masters/PhD positions in Building Optimization and Machine Learning (# of pos: 6)

Designing high-performance, low-energy, comfortable, cost-effective, robust and climate resilient buildings requires a new generation of computational tools to effectively address these challenges. These will blend aspects of physics-based building energy simulation, black-box optimization, machine learning and design space visualization. These positions will develop advances in all these areas, which will be combined into an online platform to be used by practitioners in industry and policy-makers in government to deliver better building designs that meet stringent new targets in British Columbia.

The positions available (see below) will be supervised by Dr Ralph Evins (previously of Imperial College London and ETH Zurich) in his Energy Systems and Sustainable Cities research group . The work will be carried out in the stimulating multi-disciplinary environment of the Institute for Integrated Energy Systems (IESVic) and the new green Civil Engineering department at the University of Victoria on Vancouver Island in beautiful British Columbia, Canada.

A good working knowledge of Python is highly desirable for all positions (for an exceptional candidate an expert level in another programming language could be acceptable). Experience with at least one of machine learning, building energy simulation and meta-heuristic optimization is highly desirable. Mastering the English language is required.

Start dates between January and September 2019 are available. Positions are funded at a level comparable with NSERC scholarships (Master’s ; Doctoral ). Holders of these or similar fellowships are eligible for significant top-up funding.

These positions require a Bachelor’s degree (Masters applicants) or Master’s degree (PhD applicants) in engineering, computer science, mathematics or physics. The following positions are available at either level:

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  • Machine learning for building design optimization
  • This project will use machine learning methods to fit surrogate models (statistical approximations of the design space) to simulation and optimization data, and explore their use in the design process. These surrogate models will be integrated directly in the optimization process in the platform.

    Desirable skills: building simulation, machine learning

  • Robust optimization of high-performance buildings
  • High-performance building designs must be resilient to variations in user behavior and changes in future climate, or able to be easily adapted to these changes. This project will research more effective robust optimization procedures to be embedded in the platform.

    Desirable skills: building simulation, meta-heuristic optimization

  • Modelica-based optimization of high-performance buildings
  • Modelica is a new paradigm for building simulation using acausal networks of differential equations, giving unprecedented access to the computational methods used. This project will explore how this new approach can be used with design optimization and surrogate modelling.

    Desirable skills: building simulation, Modelica

  • Visualization and usability of building design tools
  • Effective data visualization approaches are needed to derive benefit from the optimizations conducted. This project will research the usability of interactive visual design exploration tools, including analysis of the unique data collected on how tools in the platform are used in practice.

    Desirable skills: building simulation, visualization methods, data analysis, survey design

  • Finding meaning in hourly building energy data using machine learning
  • This project will apply data analysis methods and machine learning models to hourly building energy use data from smart meters, assessing common patterns of behaviour within a building and across multiple buildings, to identify common threads and recommendations for improvements.

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    Desirable skills: data analysis, machine learning, building energy analysis

  • Probabilistic modelling of buildings and energy systems
  • This project will apply the latest developments in mathematical stochastic modelling techniques to problems in building and urban energy systems to help account for fluctuations due to climate, occupancy and use patterns on solar and wind availability and building thermal loads.

    Desirable skills: probabilistic analysis, building energy analysis, energy systems modelling

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