本文首发于微信公众号【运筹OR帷幄】：【学界招聘|信息】 美，欧精选OR博士（后）- 优化算法/机器学习/图像处理/数据科学
1、PhD and Postdoc position KU Leuven: Optimization frameworks for deep kernel machines
PhD and Postdoc positions KU Leuven: Optimization frameworks for deep kernel machines
The research group KU Leuven ESAT-STADIUS is currently offering 2 PhD and 1 Postdoc (1 year, extendable) positions within the framework of the KU Leuven C1 project Optimization frameworks for deep kernel machines (promotors: Prof. Johan Suykens and Prof. Panos Patrinos).
Deep learning and kernel-based learning are among the very powerful methods in machine learning and data-driven modelling. From an optimization and model representation point of view, training of deep feedforward neural networks occurs in a primal form, while kernel-based learning is often characterized by dual representations, in connection to possibly infinite dimensional problems in the primal. In this project we aim at investigating new optimization frameworks for deep kernel machines, with feature maps and kernels taken at multiple levels, and with possibly different objectives for the levels. The research hypothesis is that such an extended framework, including both deep feedforward networks and deep kernel machines, can lead to new important insights and improved results. In order to achieve this, we will study optimization modelling aspects (e.g. variational principles, distributed learning formulations, consensus algorithms), accelerated learning schemes and adversarial learning methods.
The PhD and Postdoc positions in this KU Leuven C1 project (promotors: Prof. Johan Suykens and Prof. Panos Patrinos) relate to the following possible topics:
-1- Optimization modelling for deep kernel machines
-2- Efficient learning schemes for deep kernel machines
-3- Adversarial learning for deep kernel machines
For further information and on-line applying, see
https://www.kuleuven.be/personeel/jobsite/jobs/54740654 (PhD positions) and
(click EN for English version).
2、Postdoc at University of Edinburgh in Computer Vision and Machine Learning
We are currently seeking a postdoc to work in a project in the area of visual recognition and scene understanding with a focus on multi-task learning. The project will be supervised by Dr Hakan Bilen.
Applicants should hold a PhD in computer Vision, machine learning, or closely related area. A strong research record is required, documented by publications at the top computer vision (CVPR, ICCV, ECCV) and/or machine learning conferences (NIPS, ICLR, ICML) and journals (PAMI, IJCV). We are looking for a highly motivated individual who enjoys working in an international academic research environment. Good communication skills and fluency in English, both written and spoken, are required.
- Starting date:October 2018 or later
- Duration: Two years
- Closing date: 5pm (UK time) August 10, 2018
- Salary: Grade 7:£31,604 - £38,833 p.a.
The School of Informatics at Edinburgh is one of the top-ranked Computer Science departments in Europe and offers an exciting research environment. Edinburgh is a beautiful historic city with a high quality of life.
Please apply via the university vacancy page(https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=044420 ) and also send a notification e-mail to the email address below with the title [Postdoc Candidate], including:
- a brief statement of research interests (describing how past experience and future plans fit with the advertised position)
- complete CV, including list of publications
- the names and email addresses of two references
3、Postdoc Positions in Machine Learning at TU Kaiserslautern
Multiple Postdoc and PhD positions in Statistical Machine Learning at the Machine Learning Group, Department of Computer Science, TU Kaiserslautern
※ Deadline: none (asap) ※
The machine learning group at the Department of Computer Science at TU Kaiserslautern (headed by Prof. Marius Kloft,（http://ml.informatik.uni-kl.de/） is recruiting highly-motivated doctoral and postdoctoral researchers.
※ MEET ME AT ICML 2018 ※
I am at ICML right now. One of the best ways to inquire about the job is to meet me at ICML. E.g., you can meet me at our posters 54 and 147 or contact me via *Whova*.
※ ABOUT THE GROUP ※
The group develops theoretically grounded statistical machine learning methods for analysis of big data. The group has developed effective learning methods and statistical learning theory for learning using multiple representations (multiple kernel learning) or multiple learning tasks (multi-task learning) as well as anomaly detection.
The group has successfully applied these methods in various application domains, including network intrusion detection (Remind system), visual image recognition (ImageClef Challenge winner), computational biology (most accurate gene start finder), computational medicine (most accurate drug sensitivity predictor) and plant breeding.
The group regularly publishes on the above topics at conferences such as NIPS, ICML, AISTATS, and ECML, as well as in journals such as JMLR and MLJ.
The group's most recent research focuses on deep learning of multiple data representations, deep anomaly detection, large-scale Bayesian methods and extreme classification (multi-class learning using an extremely large number of label classes).
※ THE LOCATION ※
Kaiserslautern is one of the major locations of AI and ML research in Germany, with its Department of Computer Science and the German National Research Center for Artificial Intelligence, as well as the Deep Learning Competence Center.
The Kaiserslautern-Saarbrücken Computer Science Cluster is one of the major locations in Germany for computer science research, comprising about 800 CS researchers in two universities and ten research institutes (including two Max-Planck and two Fraunhofer institutes).
※ HOW TO APPLY ※
The candidates are expected to conduct fundamental machine learning research (usually by developing new algorithms or theory) in one of the groups' core areas (described above). Successful candidates can be given the opportunity to gain teaching experience and to co-advise undergraduate and M.Sc. students and work with other PhD students and Postdocs.
The group draws from a network of international collaboration partners (academic and industrial) in Europe, US, and Asia.
Collaboration with the partners is encouraged.
Applications are submitted by email to
kloft [at] cs [dot] uni-kl [dot] de using subject
'Application for the PhD Position KL2-ML-PhD" or
'Application for the Postdoc Position KL2-ML-PostDoc"
Applications shall include a link to the supporting documents, which are asked to include a signed cover/motivation letter mentioning the job ad number, a CV, supporting letters or references, a pdf of the BSc and MSc theses, evidence for mathematical skills (e.g., grades, mathematical thesis, etc.), and - ideally - a research statement (mandatory for postdoc applications).
The final contract will given to successful applicants by the recruitment department of TU Kaiserslautern.
The duration of the contract depends on the position, the contract, and the funding source. Payment is according to the competitive German TVL E-13 payment scheme, depending on the candidate's experience and qualifications.
Applicants are cordially invited to contact Marius Kloft by email for further inquiries. Please use the subject stated above
4、Postdoctoral associate (UVa): imaging and data science
The Departments of Biomedical Engineering and Electrical and Computer Engineering at the University of Virginia seek Research Associates to work in the laboratory of Dr. Gustavo Rohde (http://imagedatascience.com) to perform research
oncomputational predictive modeling, and image and signal analysis with applications to biomedical imaging, cancer detection, mobile and remote sensing, and others. The Research Associate will be expected to design, implement, and test mathematical algorithms for solving problems related to image reconstruction, signal and image classification, and/or bioinformatics.
Candidates must hold a PhD in Biomedical Engineering, Electrical and Computer Engineering, Computer Science, or a closely related discipline by the start date. Candidates must also show evidence of research productivity (e.g. papers, patents, presentations, etc), and have the ability to implement computer algorithms. To apply, visit https://jobs.virginia.edu/applicants/Central?quickFind=84835 Complete a candidate profile on-line; attach a CV, cover letter, contact information for three references and a statement of Research Interest. The position will remain open until filled.
The University of Virginia is an affirmative action/equal opportunity employer. Women, minorities, veterans and persons with disabilities are encouraged to apply.