Subject: [AUDITORY] Funded PhD in Machine Listening at Queen Mary University of London From: Emmanouil Benetos <emmanouil.benetos@xxxxxxxx> Date: Fri, 15 Jun 2018 15:01:04 +0100 List-Archive:<http://lists.mcgill.ca/scripts/wa.exe?LIST=AUDITORY>This is a multi-part message in MIME format. --------------5D307D94464DD6D830ECE614 Content-Type: text/plain; charset=utf-8; format=flowed Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by edgeum2.it.mcgill.ca id w5FE1BIo012907 Dear all, A funded PhD place in machine listening / computer audition is available=20 at the School of Electronic Engineering and Computer Science, Queen Mary=20 University of London. Full details below, or at=20 https://www.findaphd.com/search/ProjectDetails.aspx?PJID=3D98854 . Best, Emmanouil Benetos *Funded PhD studentship in Machine Listening at the School of Electronic=20 Engineering and Computer Science, Queen Mary University of London* Applications are invited from all nationalities for a funded PhD=20 Studentship starting Autumn 2018 in machine listening / computer=20 audition, to conduct research in the area of computational sound scene=20 analysis. RESEARCH PROJECT: This research will investigate and prototype tools for=20 sound event detection and audio context recognition from everyday sound=20 scenes. This PhD position is linked with the EPSRC-funded project=20 "Integrating sound and context recognition for acoustic scene analysis"=20 on developing technologies for context-aware sound recognition. The=20 successful candidate will investigate, propose and develop machine=20 learning and digital signal processing methods for sound recognition,=20 suitable for complex and time-varying acoustic environments. SKILLS: Candidates must have a first-class honours degree or equivalent,=20 and/or a good MSc Degree in Computer Science, Electronic Engineering,=20 Audio/Music Technology, Acoustics, or a related discipline. Candidates=20 should have good programming experience in Python, Matlab, C/C++ or=20 similar. Knowledge of machine learning and/or digital signal processing=20 is desirable. Experience in research and a track record of publications=20 is advantageous. There is scope to tailor the research to the interests=20 and skills of the successful candidate. SUPERVISION: This project is based in the Centre for Digital Music=20 (C4DM) and Centre for Intelligent Sensing (CIS) of Queen Mary University=20 of London. C4DM is a world-leading multidisciplinary research group in=20 the field of Digital Music & Audio Technology; CIS has highly reputed=20 research expertise in multi-sensor data processing, distributed signal=20 processing, vision and audio analysis. Both groups are part of the=20 School of Electronic Engineering and Computer Science (EECS). Details=20 about the School can be found at http://www.eecs.qmul.ac.uk; details=20 about C4DM at http://c4dm.eecs.qmul.ac.uk; and details about CIS at=20 http://cis.eecs.qmul.ac.uk/ . FUNDING: The studentship is for 3 years, and covers student fees as well=20 as a tax-free stipend of 16,777 GBP per annum. To apply, please follow the on-line process at=20 (https://www.qmul.ac.uk/postgraduate/research/subjects/); click on the=20 list of Research Degree Subjects, select =E2=80=98Electronic Engineering=E2= =80=99 in the=20 =E2=80=98A-Z list of research opportunities=E2=80=99, and follow the inst= ructions on the=20 right-hand side of the web page. Please note that instead of the =E2=80=98Research Proposal=E2=80=99 we re= quest a=20 Statement of Research Interests=E2=80=99. Your statement should answer tw= o=20 questions: (i) Why are you interested in the topic described above? (ii)=20 What relevant experience do you have? Your statement should be brief: no=20 more than 500 words or one side of A4 paper. In addition we would also=20 like you to send a sample of your written work (e.g. excerpt of final=20 year dissertation or published academic paper). More details can be=20 found at: http://www.eecs.qmul.ac.uk/phd/how-to-apply . Informal=20 enquiries about the studentship can be made by email to Dr Emmanouil=20 Benetos (emmanouil.benetos@xxxxxxxx). The closing date for the applications is 6 July 2018; interviews are=20 expected to take place in mid-July 2018. --=20 Dr Emmanouil Benetos School of Electronic Engineering and Computer Science Queen Mary University of London Tel: +44 (0)20 7882 3066 e-mail: emmanouil.benetos@xxxxxxxx http://www.eecs.qmul.ac.uk/~emmanouilb/ --------------5D307D94464DD6D830ECE614 Content-Type: text/html; charset=utf-8 Content-Transfer-Encoding: quoted-printable X-MIME-Autoconverted: from 8bit to quoted-printable by edgeum2.it.mcgill.ca id w5FE1BIo012907 <html> <head> <meta http-equiv=3D"content-type" content=3D"text/html; charset=3Dutf= -8"> </head> <body text=3D"#000000" bgcolor=3D"#FFFFFF"> Dear all,<br> <br> A funded PhD place in machine listening / computer audition is available at the School of Electronic Engineering and Computer Science, Queen Mary University of London.<br> Full details below, or at <a class=3D"moz-txt-link-freetext" href=3D"https://www.findaphd.com/s= earch/ProjectDetails.aspx?PJID=3D98854">https://www.findaphd.com/search/P= rojectDetails.aspx?PJID=3D98854</a> .<br> <br> Best,<br> Emmanouil Benetos<br> <br> <br> <b>Funded PhD studentship in Machine Listening at the School of Electronic Engineering and Computer Science, Queen Mary University of London</b><br> <br> Applications are invited from all nationalities for a funded PhD Studentship starting Autumn 2018 in machine listening / computer audition, to conduct research in the area of computational sound scene analysis.<br> <br> RESEARCH PROJECT: This research will investigate and prototype tools for sound event detection and audio context recognition from everyday sound scenes. This PhD position is linked with the EPSRC-funded project "Integrating sound and context recognition for acoustic scene analysis" on developing technologies for context-aware sound recognition. The successful candidate will investigate, propose and develop machine learning and digital signal processing methods for sound recognition, suitable for complex and time-varying acoustic environments.<br> <br> SKILLS: Candidates must have a first-class honours degree or equivalent, and/or a good MSc Degree in Computer Science, Electronic Engineering, Audio/Music Technology, Acoustics, or a related discipline. Candidates should have good programming experience in Python, Matlab, C/C++ or similar. Knowledge of machine learning and/or digital signal processing is desirable. Experience in research and a track record of publications is advantageous. There is scope to tailor the research to the interests and skills of the successful candidate.<br> <br> SUPERVISION: This project is based in the Centre for Digital Music (C4DM) and Centre for Intelligent Sensing (CIS) of Queen Mary University of London. C4DM is a world-leading multidisciplinary research group in the field of Digital Music & Audio Technology; CIS has highly reputed research expertise in multi-sensor data processing, distributed signal processing, vision and audio analysis. Both groups are part of the School of Electronic Engineering and Computer Science (EECS). Details about the School can be found at <a class=3D"moz-txt-link-freetext" href=3D"http://www= .eecs.qmul.ac.uk">http://www.eecs.qmul.ac.uk</a>; details about C4DM at <a class=3D"moz-txt-link-freetext" href=3D"http://c4dm.eecs.qmul.ac.u= k">http://c4dm.eecs.qmul.ac.uk</a>; and details about CIS at <a class=3D"moz-txt-link-freetext" href=3D"http://cis.eecs.qmul.ac.uk= /">http://cis.eecs.qmul.ac.uk/</a> . <br> <br> FUNDING: The studentship is for 3 years, and covers student fees as well as a tax-free stipend of 16,777 GBP per annum.<br> <br> To apply, please follow the on-line process at (<a class=3D"moz-txt-link-freetext" href=3D"https://www.qmul.ac.uk/po= stgraduate/research/subjects/">https://www.qmul.ac.uk/postgraduate/resear= ch/subjects/</a>); click on the list of Research Degree Subjects, select =E2=80=98Electronic Engineering=E2=80=99 in the =E2=80=98A-Z list of research opportuniti= es=E2=80=99, and follow the instructions on the right-hand side of the web page.<br> <br> Please note that instead of the =E2=80=98Research Proposal=E2=80=99 w= e request a Statement of Research Interests=E2=80=99. Your statement should answe= r two questions: (i) Why are you interested in the topic described above? (ii) What relevant experience do you have? Your statement should be brief: no more than 500 words or one side of A4 paper. In addition we would also like you to send a sample of your written work (e.g. excerpt of final year dissertation or published academic paper). More details can be found at: <a class=3D"moz-txt-link-freetext" href=3D"http://www.eecs.qmul.ac.uk= /phd/how-to-apply">http://www.eecs.qmul.ac.uk/phd/how-to-apply</a> . Info= rmal enquiries about the studentship can be made by email to Dr Emmanouil Benetos (<a class=3D"moz-txt-link-abbreviated" href=3D"mailto:emmanouil.benet= os@xxxxxxxx">emmanouil.benetos@xxxxxxxx</a>).<br> <br> The closing date for the applications is 6 July 2018; interviews are expected to take place in mid-July 2018.<br> <br> <br> <pre class=3D"moz-signature" cols=3D"72">--=20 Dr Emmanouil Benetos School of Electronic Engineering and Computer Science Queen Mary University of London Tel: +44 (0)20 7882 3066=20 e-mail: <a class=3D"moz-txt-link-abbreviated" href=3D"mailto:emmanouil.be= netos@xxxxxxxx">emmanouil.benetos@xxxxxxxx</a>=20 <a class=3D"moz-txt-link-freetext" href=3D"http://www.eecs.qmul.ac.uk/~em= manouilb/">http://www.eecs.qmul.ac.uk/~emmanouilb/</a></pre> </body> </html> --------------5D307D94464DD6D830ECE614--