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CFP META'2020 @Marrakech
by El-ghazali Talbi 14 Jan '20

14 Jan '20

Dear colleagues, please distribute the CFP of META'2020 in your networks. Thanks for your collaboration. Apologies for cross-posting. Appreciate if you can distribute this CFP to your network. ********************************************************************** META'2020 International Conference on Metaheuristics and Nature Inspired Computing 27-31 Oct 2020 Marrakech, Morocco http://meta2020.sciencesconf.org/ ********************************************************************** META is one of the main event focusing on the progress of the area of metaheuristics and their applications. As in previous editions, META’2020 will provide an opportunity to the international research community in metaheuristics to discuss recent research results, to develop new ideas and collaborations, and to meet old friends and make new ones in a friendly and relaxed atmosphere. All selected long papers will be published in the Springer (SCOPUS, ISI, DBLP) conference proceedings. At least, 2 special special issues in ISI and SCOPUS journals are also confirmed. META'2020 welcomes presentations that cover any aspects of metaheuristic research such as new algorithmic developments, high-impact applications, new research challenges, theoretical developments, implementation issues, and in-depth experimental studies. META'2020 strives for a high-quality program that will be completed by a number of invited talks, tutorials, workshops and special sessions. The scope of the META’2020 conference includes, but is not limited to: * Local search, tabu search, simulated annealing, VNS, ILS, … * Evolutionary algorithms, swarm optimization, bio-inspired algorithms, … * Emergent nature inspired algorithms: quantum computing, artificial immune systems, bee colony, DNA computing, … * Quantum computing * Parallel and distributed algorithms * Hybrid methods with machine learning, game theory, mathematical programming, constraint programming, co-evolutionary, … * Application to: logistics and transportation, networks, scheduling, data mining, engineering design, energy, cloud, bio-medical, … * Theory of metaheuristics, landscape analysis, convergence, problem difficulty, very large neighbourhoods, … * Multi-objective optimization, bi-level optimization * Dynamic optimization, problems with uncertainty, … * Parameter tuning (static, dynamic, adaptive, self-adaptive) * Hyper-heuristics, cross-domain metaheuristics * Software frameworks for metaheuristics and nature inspired computing Submission of papers: ____________________ - Submission of papers via the website S1) Short papers: Extended abstracts of work-in-progress and position papers of a maximum of 3 pages. S2) Long papers: Original research contributions of a maximum of 10 pages. S3) Journal papers: High-quality manuscripts that have recently, within the last year, been submitted or accepted for journal publication. For this special case, the submission should have the same title that the accepted work, the abstract AND the complete reference of the work. - Proceedings of the conference will be provided. Selected papers of type S2 will be published in a Springer book (SCOPUS, ISI, DBLP). - Predefined styles are available on the website http://meta2020.sciencesconf.org/ - Proposals for invited sessions, workshops and tutorials: Deadline 30th March 2020, contact meta2020(a)sciencesconf.org Important dates _______________ - Submission deadline: April 24, 2020 - Notification of acceptance: May 30, 2020 -- *********************************************************************** META'2020 International Conference on Metaheuristics and Nature Inspired Computing 28-31 Oct 2020, Marrakech, Morocco http://meta2020.sciencesconf.org *********************************************************************** Prof. El-ghazali TALBI Polytech'Lille, University Lille - INRIA CRISTAL - CNRS

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IEEE/ACM SPECIAL ISSUE ANNOUNCEMENT
by PietroHiram Guzzi 14 Jan '20

14 Jan '20

(APOLOGIZE FOR MULTIPLE POSTING) CALL FOR PAPERS IEEE Transactions on IEEE/ACM Transactions on Computational Biology and Bioinformatics Special Issue/Section on Deep Learning and Graph Embeddings for Network Biology GUEST EDITORS: Pietro Hiram Guzzi Associate Professor, University of Catanzaro, Department of Surgical and Medical Sciences hguzzi(a)unicz.it Marinka Zitnik Assistant Professor, Harvard University, Department of Biomedical Informatics marinka(a)hms.harvard.edu TOPIC SUMMARY: Biological Networks are powerful resources for modelling, analysis, and discovery in biological systems, ranging from molecular to epidemiological levels. In recent years, network models and algorithms have been used to represent and analyse the whole set of associations and interactions among biologically relevant molecules inside cells, (e.g., proteins, genes, transcription factors and more recently the big class of non-coding genes), supporting the elucidation of the molecular mechanisms as well as the development of precision medicine for many relevant diseases (e.g., cancers or brain disorders). Mathematical machinery that is central to this area of research is graph theory and machine learning on graph-structured data. Recent research efforts have introduced methods and tools that can model biological phenomena and learn and reason about them through networks. Such data and models are typically stored in various databases of experimental data and repositories of biomedical knowledge. Network data extracted from these databases are often mined for knowledge about a biological system of interest (e.g., using network statistics or community detection algorithms) and to compare two or more networks (e.g., using network alignment algorithms). Current approaches may present limitations in some applications since they can fail to generalize from observed network structure to new biological phenomena, are unable to include prior knowledge in the analysis, rely on user-defined heuristics and painstaking manual feature engineering to extract features from biological networks, or fail to support researchers when limited biological data is available (e.g., small datasets with low coverage). Recent years have seen a surge in approaches, such as deep learning, that have shown broad utility in uncovering new biology and contributing to new discoveries in wet laboratory experiments. In particular, in biological and biomedical area, deep learning have evidenced an efficient way to deal with data generated from modern high-throughput technologies. In parallel, the field of network science has been influenced by the development of methods that automatically learn to encode network structure into low-dimensional embeddings, using data transformation techniques based on matrix factorization, deep learning, nonlinear dimensionality reduction, and complex non-linear models. The key idea of these methods (or graph representation learning) is to automatically learn a function able to map nodes in the graph (or other graph structures) to points in a compact vector space, whose geometry is optimized to reflect topology of the input graph. The relevance and potential of graph representation learning are evidenced by the rise of approaches that are beginning to effect on the way network biology is performed today at the fundamental level. Therefore, there is strong need to discuss and foster these advances in a systematic way to give support both to researchers and practitioners. The goal of this special issue is to collect both surveys and papers describing novel methods and applications in computational biology and bioinformatics. Papers presenting applications in medicine and healthcare are also welcome. The topics of interest for this special issue include, but are not limited to: * Deep learning and graph neural networks for network biology * Learning meaningful representations for biomedical networks * Learning node, edge, higher-order, and graph-level embeddings for biological networks * Next-generation graph embedding techniques for important problems, including node classification, link prediction, graph classification, network alignment and beyond * Graph representation learning for visualizing and interpreting interaction data * Next-generation network science through network embeddings * Relevant benchmark datasets, initial solutions for new challenges and new directions in network biology * Applications of network embeddings broadly in computational biology, genomics, medicine, and health IMPORTANT DATES: Abstract submission: Jan-Feb 2020 Open for submissions in ScholarOne Manuscripts: February 28 2020 Closed for submissions: July 30, 2020 Results of first round of reviews: September 15, 2020 Submission of revised manuscripts: October 15, 2020 Results of second round of reviews: November 15, 2020 Publication materials due: December 15, 2020 SUBMISSION GUIDELINES: Prospective authors are invited to submit their manuscripts electronically after the Òopen for submissionsÓ date, adhering to the IEEE/ACM Transactions in Computational Biology and Bioinformatics guidelines (http://www.computer.org/portal/web/tcbb-cs/author) Please submit your papers through the online system (https://mc.manuscriptcentral.com/tcbb-cs) and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by e-mail to the Guest Editors directly. GUEST EDITORS BIOGRAPHIES: Pietro Hiram Guzzi Pietro H. GuzziÊis an Associate Professor of Computer Science and Bioinformatics at the University ÔMagna Gr¾ciaÕ of Catanzaro, Italy, since 2008. He received his PhD in Biomedical Engineering in 2008, from Magna Gr¾cia University of Catanzaro. He received his Laurea degree in Computer Engineering in 2004 from the University of Calabria, Rende, Italy. His research interests comprise bioinformatics, network analysis. In network analysis, in particular, Pietro has worked on local alignment of biological networks providong some tools for network alignment. Actually is working on novel approaches of alignment that merge together both local and global alignment and on the development of novel methods of analysis based on the integration of heterogeneous networks thorugh embedding. Pietro is an ACM member and serves the scientific community as reviewer for many conferences. He is associate editor of IEEE/ACM TCBB, and of SIGBioinformatics Record. Marinka Zitnik Marinka Zitnik is an Assistant Professor at Harvard University. Her research investigates artificial intelligence and machine learning to advance science, medicine, and health. Her methods have had a tangible impact in biology, genomics, and drug discovery, and are used by major biomedical institutions, including Baylor College of Medicine, Karolinska Institute, Stanford Medical School, and Massachusetts General Hospital. Before Harvard, she was a postdoctoral scholar in Computer Science at Stanford University and a member of the Chan Zuckerberg Biohub at Stanford. She received her Ph.D. in Computer Science from University of Ljubljana while also researching at Imperial College London, University of Toronto, Baylor College of Medicine, and Stanford University. Her work received several best paper, poster, and research awards from the International Society for Computational Biology. She has recently been named a Rising Star in EECS by MIT and also a Next Generation in Biomedicine by The Broad Institute of Harvard and MIT, being the only young scientist who received such recognition in both EECS and Biomedicine.

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GSK Data Science/Bioinformatics positions
by Fernando Ulloa Montoya 13 Jan '20

13 Jan '20

We have 3 exciting positions at GSK Vaccines in Rixensart, take a look at: Data Scientist: https://jobs.gsk.com/nl-be/jobs/236345?lang=en-us&previousLocale=nl-BE Senior Data Scientist: https://jobs.gsk.com/nl-be/jobs/223722?lang=en-us&previousLocale=nl-BE Senior Manager Data Science: https://jobs.gsk.com/nl-be/jobs/236342?lang=en-us&previousLocale=nl-BE Best regards, Fernando GSK monitors email communications sent to and from GSK in order to protect GSK, our employees, customers, suppliers and business partners, from cyber threats and loss of GSK Information. GSK monitoring is conducted with appropriate confidentiality controls and in accordance with local laws and after appropriate consultation.

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Postdoc: Computational Biology
by Lodewyk Wessels 12 Jan '20

12 Jan '20

[image: New CellCircuits Graphic Add Jan2020 V2.jpg]

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PhD and Postdoc positions
by Joana Gonçalves -EWI 10 Jan '20

10 Jan '20

Dear All, We have new vacancies for a PhD candidate and two postdoc positions: * PhD candidate (4-year contract) on learning algorithms for cancer therapeutic target discovery leveraging heterogeneous molecular data and large-scale loss-of-function screens. Opportunity to pursue machine learning challenges such as learning across different domains, and/or delve into topics like causal inference. Application at https://www.academictransfer.com/en/288752/phd-learning-algorithms-for-drug…. * Postdoc positions (2-year contract) in a flagship collaboration between the TU Delft, the Erasmus MC, and the Holland Proton Therapy Center: on single-cell discovery of biomarkers for targeted proton therapy of head and neck cancer. The position at Erasmus MC will have an experimental focus on using single-cell DNA and RNA sequencing and photoselection for phenotypic labeling of DNA repair dynamics to characterize (un)treated tumor cell line and patient samples. The position at the TU Delft is computational and focuses on the modelling of single-cell response to treatment and discovery of molecular and phenotypic biomarkers conferring resistance to therapy. Application at https://convergence.healthandtechnology.nl/square/position/37. The application deadline for the PhD position is 9 February 2020. For the postdoc positions, recruitment will take place until the positions are filled. Please feel free to contact me for additional information, or to send the vacancies around to potentially interested candidates. Kind regards, Joana -- Joana Gonçalves Assistant Professor TU Delft Faculty EEMCS, Dept. Intelligent Systems Van Mourik Broekmanweg 6, 2628 XE Delft Building 28, Office E05.060 http://joanagoncalves.org

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Fwd: [Bioinf] PhD position Bioinformatics and Oncology, at NKI and VU
by Sanne Abeln 03 Jan '20

03 Jan '20

Dear All, Please find below a link to a PhD position in Bioinformatics and Oncology. This PhD position is part of a collaborative project between the NKI and the VU to discover the role of chromosomal breaks in colorectal cancer. Machine learning and sequence analysis techniques will be used to analyse the impact of chromosomal breaks. https://workingat.vu.nl/ad/phd-position-in-bioinformatics-and-oncology/umau… The application deadline is 31 January 2020 Best wishes, Sanne -- Sanne Abeln Associate Professor Bioinformatics - VU University Amsterdam s.abeln(a)vu.nl +31 20 59 87816 www.few.vu.nl/~abeln -- Sanne Abeln Associate Professor Bioinformatics - VU University Amsterdam s.abeln(a)vu.nl +31 20 59 87816 www.few.vu.nl/~abeln

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Postdoctoral Scientist Bioinformatics in Multiple Sclerosis - KU Leuven
by An Goris 23 Dec '19

23 Dec '19

Dear all, Our laboratory is currently recruiting a postdoctoral scientist bioinformatics for research in the field of multiple sclerosis (application by 3 Jan 2020). Please find all details attached, application is through the online KU Leuven application tool, please see: https://www.kuleuven.be/personeel/jobsite/jobs/55487847 For more information, please feel free to contact me. Best wishes, An An Goris, PhD Professor Laboratory for Neuroimmunology KU Leuven Herestraat 49 bus 1022 B-3000 Leuven, Belgium an.goris(a)kuleuven.be<mailto:an.goris@kuleuven.be>; Phone: +32-16-33.07.72. http://gbiomed.kuleuven.be/lni

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PhD position in medical bioinformatics in Brussels
by Wim VRANKEN 20 Dec '19

20 Dec '19

Dear all, Please find attached information about an open Ph.D. position in bioinformatics. Happy holidays! Wim --------------------------------------------------------------------------------------- Prof. Dr. Wim Vranken Tel: +32 (0)2 6291952 (VUB) +32 (0)2 6505943 (IB)2 VUB - Building E, 5th Floor, E5.02, Pleinlaan 2 1050 Brussels, Belgium Structural Biology Brussels/Artificial Intelligence/General Chemistry/Biomedical sciences Research professor - http://bio2byte.be/ ULB-VUB - Interuniversity Institute of Bioinformatics in Brussels (IB)2 Director - http://ibsquare.be/ ---------------------------------------------------------------------------------------

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Job opening for a PhD student in bioinformatics for structural protein modeling
by Geert Vandeweyer 20 Dec '19

20 Dec '19

Dear colleagues, There is a new vacancy for a doctoral researcher in the area of bioinformatics for structural protein modeling. Feel free to send it around. https://www.uantwerpen.be/en/jobs/vacancies/ap/2019bapffbdef339/ Kind regards, Geert -- Geert Vandeweyer, Ph.D. Department of Medical Genetics Cognitive Genetics University of Antwerp Prins Boudewijnlaan 43/6 2650 Edegem Belgium Tel: +32 (0)3 275 97 56 E-mail: geert.vandeweyer(a)uantwerpen.be<mailto:geert.vandeweyer@uantwerpen.be> https://www.uantwerpen.be/en/rg/cognet/ http://www.linkedin.com/pub/geert-vandeweyer/26/457/726 -- Email Efficiency Disclaimer: I read & answer emails between 8 AM and 9 AM. I'm not availabe on thursdays. For urgent matters during the day, call me. --

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Job opening for a PhD student in bioinformatics for ecosystem monitoring.
by Kris Laukens 20 Dec '19

20 Dec '19

Dear colleagues, there is a new vacancy for a doctoral researcher in the area of bioinformatics for ecosystem monitoring. Feel free to send it around. http://bit.ly/2PHUmUv Kind regards, Kris -- Kris Laukens, PhD Associate professor bioinformatics - biodata mining Adrem data lab Biomedical Informatics Network Antwerpen (biomina) Department of Mathematics and Computer Science University of Antwerp Middelheimlaan 1, G.111 B-2020 Antwerpen, Belgium T +32 (0)3 265 33 10 E kris.laukens(a)uantwerpen.be<mailto:kris.laukens@uantwerpen.be> W www.uantwerpen.be/en/staff/kris-laukens/<http://www.uantwerpen.be/en/staff/kris-laukens/>

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