Adversarial Variational Learning
ETH Zurich is one of the world’s leading universities specialising in science and technology. It is renowned for its excellent education, its cutting-edge fundamental research and its efforts to put new knowledge and innovations directly into practice.
The main goal of the Swiss Data Science Center is to accelerate the adoption of data science in academia and industry. It is an organization within ETH Zürich and EPFL, funded by ETH board. We are 30+ researchers working on machine learning applications ranging from climate science to medical diagnostic. We also collaborate with the industry in numerous projects from predictive maintenance to natural language processing. We are currently expanding the number of projects and our staff to be able to provide our research capabilities to over 20 projects.
About WindMill: With their evolution towards 5G and beyond, wireless communication networks are entering an era of massive connectivity, massive data, and extreme service demands. A promising approach to successfully handle such a magnitude of complexity and data volume is to develop new network management and optimization tools based on machine learning. This is a major shift in the way wireless networks are designed and operated, posing demands for a new type of expertise that requires the combination of engineering, mathematics and computer science disciplines. The ITN project WindMill addresses this need by providing Early Stage Researchers (ESRs) with an expertise integrating wireless communications and machine learning. The project will train 15 ESRs within a consortium of leading international research institutes and companies comprising experts in wireless communications and machine learning. This a very timely project, providing relevant inter-disciplinary training in an area where machine learning represents a meaningful extension of the current methodology used in wireless communication systems. Accordingly, the project will produce a new generation of experts, extremely competitive on the job market, considering the scale by which machine learning will impact the future and empower the individuals that are versed in it.
Early Stage Researcher / PhD student: Implicit generative modeling for communication networks
ETH is seeking to hire an Early Stage Researcher (ESR) to join the WindMill project. The project offers an excellent research and training program: the ESR will have the opportunity to join a network of leading Universities, research institutes and companies in the field of wireless communications and machine learning. The ESR is primarily hosted by Aalto University with stays in partner institutions (secondments). He/she will join a training program including regular summer and winter schools to build technical skills as well as soft skills. The salary is very competitive and composed by the following allowances: living, mobility and family (the latest only of eligible, see criteria of eligibility in the project website). The ESR will be enrolled in the Computer Science PhD program at ETH Zürich.
Title Adversarial Variational Learning
Objectives: Implicit generative modeling, in general, and Generative Adversarial Networks (GANs), in particular, promise to solve the universal simulator problem in an end-to-end fashion GANs aim at providing an indefinite number of samples with the same statistical properties as the training set. This opens a new avenue for defining simulators for any physical phenomena that might not be well understood or data cannot be drawn at will, which can then be studied at a lab with simulated experiments. GANs have been successfully applied to a variety of tasks, such as image-to-image translation, image super-resolution, image in-painting, domain adaptation, text-to-image synthesis, dark matter estimation, and breaking federated learning systems, among many others. In this proposal the PhD candidate will advance in two different directions. First in the understanding and definition of GANs and VAEs and solve long standing problems in their convergence and marginal likelihood computation. Second, its application to communication networks, which have grown into a complex ecosystem in which implicit modeling would provide the right set of tools to analyze them.
Expected Results: The ESR will create invent new algorithms for training VAEs and GANs, will study new deep neural network structures for communications networks, and will develop algorithms for estimating the likelihood of the generated examples. Early Stage Researchers/PhD student positions part of WindMill project (Integrating WIreless Communication Engineering and MachIne Learning). WindMill is an international, multidisciplinary and multisectorial training network funded by the European Union's Horizon2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No813999. More information https://windmill-itn.eu/.
The candidate holds an undergraduate degree and Master’s degree (or equivalent) in Electrical Engineering or Computer Science. Strong background in wireless communications and/or machine learning is an advantage. He/she has excellent writing and verbal communication skills, as well as presentation skills. Besides proficiency in English, excellent organizational skills, creativity, innovative and independent thinking is are must. He / She shows motivation to collaborate in an interdisciplinary international team, to participate in training programs, and is willing to travel and work in and outside Europe.
All candidates must meet the following requirements to be considered for this post: Early-Stage Researchers (ESRs) shall at the time of recruitment by the host organization be in the first four years (full-time equivalent research experience) of their research careers and not yet have been awarded a doctoral degree. Full-time equivalent research experience is measured from the date when a researcher obtained the degree which would formally entitle him or her to embark on a doctorate, either in the country in which the degree was obtained or in the country in which the researcher is recruited. At the time of recruitment by the host organization, researchers must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organization for more than 12 months in the three years immediately prior to the recruitment date. Compulsory national service, short stays such as holidays and time spent as part of a procedure for obtaining refugee status (under the 1951 Geneva Convention and the 1967 Protocol) are not taken into account. If considered for the position, you will be requested to prove that you comply with those requirements.
The selection and recruitment processes of the ESRs will be in accordance with the European Charter and Code of Conduct for the Recruitment of Researchers and according to national legislation and recruitment procedures with the company/university. The recruitment process will be open, transparent, impartial, equitable, and merit-based. There will be no discrimination based on race, gender, sexual orientation, religion or belief, disability or age. The applications will be analyzed after the application deadline, and the selected candidates will be invited to a Skype interview. When you apply for this position, your application may be circulated within the consortium members as part of the hiring process. ETH Zürich reserves the right for justified reasons to extend the application period. Application deadline: 30.04.2019.
We look forward to receiving your online application via the following link: https://emea2.softfactors.com/job-opening/rgum-6eJ4hfeNPpx6oTWbxl#/?lang=en. Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
For further information about the post, please contact Dr. Fernando Perez-Cruz (email@example.com).
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