Ph.D. position in deep learning for signal processing at University of Oldenburg
The Computational Audition Group at the Department of Medical Physics and
Acoustics, University of Oldenburg, Germany, seeks a highly motivated
PhD student
(75 % of full time, TV-L E13, 3 years, with possibility of extension)
in the field of Deep Learning for Signal Processing
Our group investigates audio signal recognition with multi-channel microphone
arrays, identifying important sound sources in real-world acoustic scenes, and
performing source separation to enhance e.g. an attended speech signal.
Applicants for the position must hold an academic university degree (master or
equivalent) in physics, engineering, computer science or a related discipline
and should have knowledge in at least one of the following fields:
- machine learning,
- speech recognition,
- acoustic event detection,
- speech and audio processing.
Excellent knowledge of scientific programming languages such as Python or
MATLAB, as well as excellent English language skills are required.
The position is funded by the newly established DFG collaborative research
centre CRC 1330 "Hearing Acoustics", which aims at improving human communication
in real-world acoustic scenes and is a long-term collaboration of researchers
from Oldenburg, Aachen and Munich. For more information about the CRC see
https://www.uni-oldenburg.de/sfb1330.
The Computational Audition Group is part of the Department of Medical Physics
and Acoustics at University of Oldenburg. We offer an international scientific
environment and world-class research facilities. For more information about the
group and the department see https://uol.de/computational-audition/ and
https://uol.de.de/en/mediphysics-acoustics/.
The University of Oldenburg is dedicated to increase the percentage of women in
science. Therefore, female candidates are strongly encouraged to apply. In
accordance to § 21 Section 3 NHG, female candidates with equal qualifications
will be preferentially considered. Applicants with disabilities will be given
preference in case of equal qualification.
Applications including a CV, a motivation letter, copies of relevant
certificates and the name and contact details of two references should be sent
as single pdf-file to joern.anemueller@uni-oldenburg.de or to
Dr. Jörn Anemüller
Faculty VI
Department of Medical Physics and Acoustics
Universität Oldenburg
D - 26111 Oldenburg
Tel. (+49) 441 798 3610
The application deadline is 28th of November 2018.
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
The Computational Audition Group at the Department of Medical Physics and
Acoustics, University of Oldenburg, Germany, seeks a highly motivated
PhD student
(75 % of full time, TV-L E13, 3 years, with possibility of extension)
in the field of Deep Learning for Signal Processing
Our group investigates audio signal recognition with multi-channel microphone
arrays, identifying important sound sources in real-world acoustic scenes, and
performing source separation to enhance e.g. an attended speech signal.
Applicants for the position must hold an academic university degree (master or
equivalent) in physics, engineering, computer science or a related discipline
and should have knowledge in at least one of the following fields:
- machine learning,
- speech recognition,
- acoustic event detection,
- speech and audio processing.
Excellent knowledge of scientific programming languages such as Python or
MATLAB, as well as excellent English language skills are required.
The position is funded by the newly established DFG collaborative research
centre CRC 1330 "Hearing Acoustics", which aims at improving human communication
in real-world acoustic scenes and is a long-term collaboration of researchers
from Oldenburg, Aachen and Munich. For more information about the CRC see
https://www.uni-oldenburg.de/sfb1330.
The Computational Audition Group is part of the Department of Medical Physics
and Acoustics at University of Oldenburg. We offer an international scientific
environment and world-class research facilities. For more information about the
group and the department see https://uol.de/computational-audition/ and
https://uol.de.de/en/mediphysics-acoustics/.
The University of Oldenburg is dedicated to increase the percentage of women in
science. Therefore, female candidates are strongly encouraged to apply. In
accordance to § 21 Section 3 NHG, female candidates with equal qualifications
will be preferentially considered. Applicants with disabilities will be given
preference in case of equal qualification.
Applications including a CV, a motivation letter, copies of relevant
certificates and the name and contact details of two references should be sent
as single pdf-file to joern.anemueller@uni-oldenburg.de or to
Dr. Jörn Anemüller
Faculty VI
Department of Medical Physics and Acoustics
Universität Oldenburg
D - 26111 Oldenburg
Tel. (+49) 441 798 3610
The application deadline is 28th of November 2018.
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Recommended by DeepMind
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✴️ @AI_Python_EN
❇️ @AI_Python
Woulda, Coulda, Shoulda: Counterfactually-Guided Policy Search" - off-policy training of agents by reasoning about alternative outcomes under counterfactual actions. https://arxiv.org/abs/1811.06272
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✴️ @AI_Python_EN
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https://lnkd.in/gxC8Awg
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[ https://lnkd.in/dW3WBi6 ]
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🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
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=> This repository is filled with IPython notebooks that cover different topics, going from Kaggle competitions to big data and deep learning.
[ https://lnkd.in/dW3WBi6 ]
☆ The Pattern Classification:
=> Tutorials and examples to solve and understand machine learning and pattern classification tasks.
[ https://lnkd.in/d9PGxHm ]
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[ https://lnkd.in/d-hNVCD ]
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✴️ @AI_Python_EN
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🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
Lectures for INFO8006 - Introduction to Artificial Intelligence, Fall 2018.
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🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
AI Pioneer Yoshua Bengio Says Universities Deserve More Credit
By Sam Shead: https://lnkd.in/ehFYW5i
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🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
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✴️ @AI_Python_EN
❇️ @AI_Python
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🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
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❇️ @AI_Python
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An archive of all O'Reilly data ebooks is available below for free download. Dive deep into the latest in data science and big data, compiled by O'Reilly editors, authors, and Strata speakers:
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To download O'Reilly data ebooks,🌎 click here.
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
There are several selections starting from 2012 Ebooks to 2016 Ebooks.
#Book #کتاب
To download O'Reilly data ebooks,🌎 click here.
🗣 @AI_Python_arXiv
✴️ @AI_Python_EN
❇️ @AI_Python
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I just got back from EMNLP in Brussels. We were presenting our dataset paper ShARC (a blog post about ShARC will be coming soon). The scale and breadth of the conference was really something, with so many smart people doing amazing things. It was also great to meet, network and talk research with all kinds of academics in NLP. We’ve got some exciting projects planned already, and I’m really just starting out. it was great to meet you all, lets stay in contact.
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Hands-On Transfer Learning with Python
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What you will learn in this book :
• Explore various DL architectures, including CNN, LSTM, and capsule networks
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🌎 Github
✴️ @AI_Python_EN
❇️ @AI_Python
Hands-On Transfer Learning with Python Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras (2018)
What you will learn in this book :
• Explore various DL architectures, including CNN, LSTM, and capsule networks
• Get to grips with models and strategies in transfer learning
• Walk through potential challenges in building complex transfer learning models from scratch
• Explore real-world research problems related to computer vision and audio analysis
• Understand how transfer learning can be leveraged in NLP
🌎 Github
✴️ @AI_Python_EN
❇️ @AI_Python