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Mastering Your PhD
  • Language: en
  • Pages: 236

Mastering Your PhD

"Mastering Your PhD: Survival and Success in the Doctoral Years and Beyond" helps guide PhD students through their graduate student years. Filled with practical advice on getting started, communicating with your supervisor, staying the course, and planning for the future, this book is a handy guide for graduate students who need that extra bit of help getting started and making it through. While mainly directed at PhD students in the sciences, the book's scope is broad enough to encompass the obstacles and hurdles that almost all PhD students face during their doctoral training. Who should read this book? Students of the physical and life sciences, computer science, math, and medicine who are thinking about entering a PhD program; doctoral students at the beginning of their research; and any graduate student who is feeling frustrated and stuck. It's never too early -- or too late! This second edition contains a variety of new material, including additional chapters on how to communicate better with your supervisor, dealing with difficult people, how to find a mentor, and new chapters on your next career step, once you have your coveted doctoral degree in hand.

Mastering Your PhD
  • Language: en
  • Pages: 156

Mastering Your PhD

  • Type: Book
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  • Published: 2009-09-02
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  • Publisher: Springer

This book helps guide PhD students through their graduate student days. Filled with practical advice on getting started, communicating with your supervisor, staying the course, and planning for the future, this book is a handy guide for graduate students who need that extra bit of help getting started and making it through. It concentrates on critical skills and tactics that are overlooked by many other how-to guides.

Mastering Your PhD
  • Language: en
  • Pages: 157

Mastering Your PhD

This book helps guide PhD students through their graduate student days. Filled with practical advice on getting started, communicating with your supervisor, staying the course, and planning for the future, this book is a handy guide for graduate students who need that extra bit of help getting started and making it through. It concentrates on critical skills and tactics that are overlooked by many other how-to guides.

Ultrashort Optical Pulses and Atomic Electron Wave Packets
  • Language: en
  • Pages: 156

Ultrashort Optical Pulses and Atomic Electron Wave Packets

  • Type: Book
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  • Published: 1990
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  • Publisher: Unknown

None

Membership Directory
  • Language: en
  • Pages: 600

Membership Directory

  • Type: Book
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  • Published: 1998
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  • Publisher: Unknown

None

SPIE ... Directory for Members
  • Language: en
  • Pages: 478

SPIE ... Directory for Members

  • Type: Book
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  • Published: 1997
  • -
  • Publisher: Unknown

None

APS Membership Directory
  • Language: en
  • Pages: 840

APS Membership Directory

  • Type: Book
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  • Published: 2000
  • -
  • Publisher: Unknown

None

Laser und Optoelektronik
  • Language: de
  • Pages: 634

Laser und Optoelektronik

  • Type: Book
  • -
  • Published: 1997
  • -
  • Publisher: Unknown

None

Locations of Knowledge in Dutch Contexts
  • Language: en
  • Pages: 336

Locations of Knowledge in Dutch Contexts

  • Type: Book
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  • Published: 2019-10-21
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  • Publisher: BRILL

Locations of Knowledge in Dutch Contexts examines how places give shape to scientific knowledge production. Contributors to this volume use four hundred years of Dutch history as laboratory to contribute to spatialized understanding of the history of knowledge.

Metalearning
  • Language: en
  • Pages: 182

Metalearning

Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience. This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.