Gå til hovedindhold
Jobbet "PhD Scholarships in Autonomous Materials Discovery" er udløbet.
Se virksomhedens profil
Se virksomheden
Vis flere job i denne kategori
Vis mig flere job
Få de nyeste job i din indbakke
Opret en jobagent nu

PhD Scholarships in Autonomous Materials Discovery (AiMade)
A number of PhD scholarships positions will be offered at the Department of Energy Conversion and Storage (DTU Energy) in connection with a new initiative on Autonomous Materials Discovery (AiMade, www.aimade.org). AiMade is a four-year cross-disciplinary project that is focused on establishing a platform for accelerated, autonomous materials discovery of clean energy materials through creation of a common data-infrastructure and holistic ontology for materials data, connecting data and information from simulations, characterization, synthesis and testing, spanning multiple time- and length-scales. The project revolves around four central competence areas plus a general machinery, involving different sections of the department and experts in various fields, from computational materials design and machine learning, to advanced synthesis and in situ characterization, and testing.

3 PhD students will be hired in relation to the project. The titles of the projects are:

  1. High temperature fuel cells and electrolysis, from metadata to longer lifetimes
  2. Inverted neural networks for optimizing permanent magnet structures
  3. Machine Learning Orchestrated Discovery and Synthesis of Organic Energy Materials

More in details, we are offering:
High temperature fuel cells and electrolysis, from metadata to longer lifetimes: Solid oxide fuel cells (SOFCs) convert the chemical energy of a fuel into electricity and heat with high electrical efficiencies. One of the main challenges for the technology is lifetime. The current project aims at utilizing the huge existing amount of durability data of different components of the device in a new approach in order to achieve an understanding of the underlying mechanisms for degradation, and using this knowledge for assessment of results obtained during field tests of SOFC/SOEC units, and providing means of innovative, accelerated testing approaches. In this project, the candidate will design and organize a comprehensive database for collection, storage and exchange of datasets related to SOFC/SOEC tests; design experiments including innovative approaches such as in-situ aggravating tests, ex-situ component ageing, in situ testing of stacks with artificially “aged” components; test of components, cells, stacks, including advanced electrochemical and micro structural analysis; model degradation based on the gained knowledge and identify means for prolonging of SOFC/SOEC lifetime. Contact person: Anke Hagen, Professor, Section for Electrochemistry.

Inverted neural networks for optimizing permanent magnet structures: Can inverted neural networks (NN) be used for optimizing permanent magnet structures? Can key properties such as magnetic field homogeneity, field magnitude and magnetic forces be assessed by an NN? If so, we may be able to open up a new area of research where creative artificial intelligence (AI) is used for making novel designs rather than using traditional and tedious optimization algorithms while at the same time clearing the path for designs and solutions hitherto un-thought of by humans. This will have a huge impact not only on magnet design, but also on any other design problem where simple fundamental building blocks combine to a complex solution. The PhD project will be focused on applying an existing numerical model of magnetostatics for training NNs, use these for predictions and then go for inverting the NNs in order to get novel/creative solutions to permanent magnet optimization challenges. Contact person: Kaspar Kirstein Nielsen, Associate Professor, Section for Continuum Modeling and Testing.

Machine Learning Orchestrated Discovery and Synthesis of Organic Energy Materials: Virtual computational screening, genetic algorithms and machine learning models have started to be successfully applied to predict and optimize properties of energy storage materials, and holds great potential for a dramatic acceleration of the development of new materials. Automated organic synthesis and reaction optimization is beginning to gain a foothold in for example the pharmaceutical industry. This interdisciplinary effort is aiming at creating and demonstrating a fully autonomous AI/ML driven development platform for new organic energy materials. The main focus of the PhD project will be the integration of spectroscopic equipment with a simple synthesis robot and autonomous, machine learning based, analysis of spectroscopic data to allow an AI/ML algorithm to follow organic syntheses or energy device preparation. Research topics include: machine learning, spectroscopic techniques and X-ray scattering methods for materials analysis, electrochemistry, aiding in the design and construction of a simple organic synthesis robot, autonomous (closed-loop) materials discovery. Contact person: Johan Hjelm, Associate Professor, Section for Electrochemistry.

The PhD projects will be carried out in close collaboration with leading international computational and experimental groups, as well as industry, and the employment will be at DTU Energy either at Lyngby or Risø campus, depending on the specific project.

We are currently present on both Risø Campus, close to the town of Roskilde, and Lyngby Campus, north of Copenhagen. In 2019, the entire department will be moving into new facilities at Lyngby Campus.

Qualifications
The successful PhD candidates should have a master's degree in physics, chemistry or engineering or possibly a degree in computer science or informatics, with an academic level equivalent to a master's degree in engineering.

A successful candidate:

  • has good communication skills in English, both written and spoken
  • is able to work independently and take responsibility for progress and quality of projects.

We favor candidates with experience in materials physics, chemistry, electrochemistry, artificial intelligence and related areas.

Approval and Enrolment
The scholarship for the PhD degree is subject to academic approval, and the candidate will be enrolled in one of the general degree programmes at DTU. For information about our enrolment requirements and the general planning of the PhD study programme, please see the DTU PhD Guide.

Assessment
The assessment of the applicants will be made by the relevant PIs and the project manager.

We offer
We offer an interesting and challenging job in an international environment focusing on education, research, public-sector consultancy and innovation, which contribute to enhancing the economy and improving social welfare. We strive for academic excellence, collegial respect and freedom tempered by responsibility. The Technical University of Denmark (DTU) is a leading technical university in northern Europe and benchmarks with the best universities in the world.

Salary and appointment terms
The salary and appointment terms are consistent with the current rules for PhD degree students. The period of employment is 3 years for the PhD projects and conditioned on the enrolment in the relevant PhD school at DTU.

You can read more about career paths at DTU here

The employments are expected to start January 1st, 2019 or soon thereafter.

Further information
If you need further information concerning these positions, please contact Project manager, Professor Tejs Vegge at teve@dtu.dk or the individual project leaders.

Please do not send applications to these e-mail addresses, instead apply online as described below.

Application
We must have your online application by 10 December 2018. Apply by clicking on the link "Ansøg". 

Remember to specify which subproject/position you are applying for (you can apply for more than one).

Applications must be submitted as one pdf file containing all materials to be given consideration. To apply, please open the link "Apply online," fill in the online application form, and attach all your materials in English in one pdf file. The file must include:

  • A letter motivating the application (cover letter)

  • Curriculum vitae

  • Grade transcripts and BSc/MSc diploma

  • Excel sheet with translation of grades to the Danish grading system (see guidelines and Excel spreadsheet here)

Candidates may apply prior to obtaining their master's degree, but cannot begin before having received it.

All interested candidates irrespective of age, gender, race, disability, religion or ethnic background are encouraged to apply.

Please write in your application that you've seen the job at Jobfinder.