Subject: Early Stage Researchers (ESRs) in Audio Restoration & Inpainting; Sound Scene Analysis; and Music source separation From: Mark D Plumbley <m.plumbley@xxxxxxxx> Date: Thu, 16 Apr 2015 08:26:14 +0000 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>Dear All, We are currently recruiting a number of "Early Stage Researcher" (ESR) positions for PhD study, including in audio and music, as part of a new "MacSeNet" EU-funded Marie Sklodowska-Curie Innovative Training Network in "Machine Sensing" (including Machine Learning, Sparse Representations and Compressed Sensing). These positions come with a competitive salary compared to regular PhD scholarships, so might also be of interest to young researchers who had not otherwise thought of taking a PhD. (Apologies for cross-posting.) Best wishes, Mark Plumbley ---- Early Stage Researchers (ESRs) in Audio Restoration & Inpainting; Sound Scene Analysis; and Music source separation University of Surrey, UK (two ESRs) and Fraunhofer IDMT (one ESR) Closing Date: 30 April 2015 Applications are invited to a number of Marie Curie Early Stage Researcher (ESR) positions as part of the new EU-funded Marie Sklodowska-Curie Actions (MSCA) Innovative Training Network (ITN) "MacSeNet: Machine Sensing Training Network". The MacSeNet ITN (http://macsenet.eu/) brings together leading academic and industry groups to train a new generation of creative, entrepreneurial and innovative early stage researchers (ESRs) in the research area of measurement and estimation of signals using knowledge or data about the underlying structure. With its combination of ideas from machine learning and sensing, we refer to this research topic as "Machine Sensing". We will apply these new methods to problems such as: advanced brain imaging; inverse imaging problems; audio and music signals; and non-traditional signals such as signals on graphs. Early Stage Researcher (ESR) positions allow the researcher to work towards a PhD, for a duration of 36 months. ESRs should be within four years of the diploma granting them access to doctorate studies at the time of recruitment, and must not have spent more than 12 months in the host country in the 3 years prior to starting. MSCA ESRs are paid a competitive salary which is adjusted for their host country. At the University Surrey, we are recruiting for two ESRs: one in Audio Restoration and Inpainting, and one in Sound Scene Analysis. At Fraunhofer IDMT, we are recruiting for one ESR, in Music source separation beyond sparse decomposition. Marie Curie ESRs are paid a competitive salary which is adjusted for their host country. For ESRs at the University of Surrey, the ESR salary including mobility allowance is equivalent to a gross salary of approximately GBP 29,500, or GBP 32,900 for ESRs with a family. For the ESR at Fraunhofer IDMT, the MSCA annual allowance is EUR 36,872 (before employer/employee tax) plus mobility and family allowance of EUR 7,200 / EUR 6,000 depending on family circumstances. More information on the audio and music ESR posts at the links below: ESR 13 : Audio Restoration and Inpainting (University of Surrey, UK) https://jobs.surrey.ac.uk/vacancy.aspx?ref=017715 ESR 14 : Sound Scene Analysis (University of Surrey, UK) https://jobs.surrey.ac.uk/vacancy.aspx?ref=017815 ESR 15 : Music source separation beyond sparse decomposition (Fraunhofer IDMT, Germany) http://www.macsenet.eu/#1|14 Informal enquires on these two posts are welcome. For ESR 13 and ESR 14 (at Surrey), please contact Dr Wenwu Wang (w.wang@xxxxxxxx) or Prof Mark Plumbley (m.plumbley@xxxxxxxx). For ESR 15 (at Fraunhofer IDMT), please contact Prof. Gerald Schuller (gerald.schuller@xxxxxxxx) More on the MacSeNet ITN at http://macsenet.eu/ There are also other ESR posts being recruited across MacSeNet, each with its own application process and closing date. The full list of Early Stage Researcher (ESR) Positions is as follows: * ESR1: Robust unsupervised learning (INRIA/CNRS/ENS Paris, France) * ESR2: Non-linear adaptive sensing/learning (INRIA/CNRS/ENS Paris, France) * ESR3: Beyond sparse representations: efficient structured representations (University of Edinburgh, UK) * ESR4: Next generation compressed sensing techniques for quantitative MRI (University of Edinburgh, UK) * ESR5: Next generation compressed sensing techniques for a fast and data-driven reconstruction of multi-contrast MRI (Technical University Munich, Germany) * ESR6: Next generation compressed sensing techniques for the fast and dynamic MRI (Technical University Munich, Germany) * ESR7: Blind source separation of functional dynamic MRI signals via distributed dictionary learning (University of Athens/Computer Technology Institute, Athens, Greece) * ESR8: Functional neuroimaging data characterisation via tensor representations (University of Athens/Computer Technology Institute, Athens, Greece) * ESR9: Phase imaging via sparse coding in the complex domain (Instituto de Telecomunicações, Portugal) * ESR10: Patch-based, non-local, and dictionary-based methods for blind image deblurring (Instituto de Telecomunicações, Portugal) * ESR11: Sparse coding in the complex domain for phase retrieval and lensless coherent diffractive imaging (Tampere University of Technology, Finland) * ESR12: Non-local HOSVD methods for denoising and super-resolution imaging (Noiseless Imaging, Finland) * ESR13: Audio Restoration and Inpainting (University of Surrey, UK) * ESR14: Sound Scene Analysis (University of Surrey, UK) * ESR15: Music source separation beyond sparse decomposition (Fraunhofer IDMT, Germany) * ESR16: Sparse models and algorithms for data on large graphs (EPFL,Switzerland) * ESR17: Towards efficient processing of 4D point clouds (EPFL,Switzerland) * ESR18: Big data analysis of time series of origin-destination (OD) matrices (VisioSafe, Switzerland) For more details of all ESR positions, and information on how to apply, see http://macsenet.eu/#1 -- Prof Mark D Plumbley Professor of Signal Processing Centre for Vision, Speech and Signal Processing (CVSSP) University of Surrey Guildford, Surrey, GU2 7XH, UK Email: m.plumbley@xxxxxxxx