#book
Computational Neuroscience (Neuromethods, 199)
This volume looks at the latest advancements in imaging neuroscience methods using magnetic resonance imaging (MRI) and electroencephalography (EEG) to study the healthy and diseased brain. The chapters in this book are organized into five parts. Parts One and Two cover an introduction to this field and the latest use of molecular models. Part Three explores neurophysiological methods for assessment, such as quantitative EEG and event-related potentials. Part Four discusses the advances and innovations made in computational anatomy, and Part Five addresses the challenges faced by researchers prior to the computational neuroscience to find wider translational applications in the field of psychiatry and mental health. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.
Computational Neuroscience (Neuromethods, 199)
This volume looks at the latest advancements in imaging neuroscience methods using magnetic resonance imaging (MRI) and electroencephalography (EEG) to study the healthy and diseased brain. The chapters in this book are organized into five parts. Parts One and Two cover an introduction to this field and the latest use of molecular models. Part Three explores neurophysiological methods for assessment, such as quantitative EEG and event-related potentials. Part Four discusses the advances and innovations made in computational anatomy, and Part Five addresses the challenges faced by researchers prior to the computational neuroscience to find wider translational applications in the field of psychiatry and mental health. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.
Title:Computational Neuroscience (Neuromethods, 199)Volume:Author(s):Drozdstoy Stoyanov (editor), Bogdan Draganski (editor), Paolo Brambilla (editor), Claus Lamm (editor)Series:Periodical:Publisher:HumanaCity:Year:2023Edition:1st ed. 2023Language:EnglishPages (biblio\tech):288\275ISBN:1071632299, 9781071632291ID:3721528Time added:2023-05-12 17:24:18Time modified:2023-05-13 18:00:11Library:Library issue:Size:9 MB (9165932 bytes)Extension:pdfWorse versions:BibTeXLinkDesr. old vers.:2023-05-12 17:24:18Edit record:Libgen LibrarianCommentary:Topic:Tags:Identifiers:ISSN:UDC:LBC:LCC:DDC:DOI:OpenLibrary ID:Google Books:ASIN:Book attributes:DPI:OCR:Bookmarked:Scanned:Orientation:Paginated:Color:Clean:0yesyesMirrors:Libgen & IPFS & TorLibgen.liGnutellaEd2kDC++Torrent per 1000 filesThis volume looks at the latest advancements in imaging neuroscience methods using magnetic resonance imaging (MRI) and electroencephalography (EEG) to study the healthy and diseased brain. The chapters in this book are organized into five parts. Parts One and Two cover an introduction to this field and the latest use of molecular models. Part Three explores neurophysiological methods for assessment, such as quantitative EEG and event-related potentials. Part Four discusses the advances and innovations made in computational anatomy, and Part Five addresses the challenges faced by researchers prior to the computational neuroscience to find wider translational applications in the field of psychiatry and mental health. In the Neuromethods series style, chapters include the kind of detail and key advice from the specialists needed to get successful results in your laboratory.
Cutting-edge and comprehensive, Computational Neuroscience is a valuable tool for researchers in the psychiatry and mental health fields who want to learn more about ways to incorporate computational approaches into utility and validity of clinical methods.
Table of contents :
Preface to the Series
Preface
Contents
Contributors
Part I: Introduction
Chapter 1: Toward Methodology for Strategic Innovations in Translational and Computational Neuroscience in Psychiatry
1 Background
2 Current Advancements
2.1 Future Research Goals
2.2 Expected Results
3 Impact
References
Part II: Molecular Methods
Chapter 2: Molecular Methods in Neuroscience and Psychiatry
1 Introduction
2 Methods
3 Results and Discussions
3.1 Methods in Neurotrannoscriptomics
3.2 Methods in Neuroproteomics
3.3 Methods in Epigenetics
3.4 Flow Cytometry in Psychiatry and Neuroscience
3.5 ELISpot/FluoroSpot in Psychiatry and Neuroscience
4 Conclusions
References
Chapter 3: Toward the Use of Research and Diagnostic Algorithmic Rules to Assess the Recurrence of Illness and Major Dysmood D...
1 Mood Disorder Concepts: The Ultimate Chaos
2 Diagnosis of Mood Disorders: The Ultimate Chaos
3 Lack of a Correct Model Prevents Targeted Research
4 Machine Learning Models
5 RADAR Scores and Plots
6 Why the Diagnosis ``Bipolar Disorder´´ Is Useless
6.1 Patients with BP1 and BP2 May Be Classified as SMDM or MDMD
6.2 Depressive and Manic Episodes Are Manifestations of ROI
6.3 The Diagnoses of MDD, MDE, BP1, and BP2 Are Irrelevant in Our Precision Models
6.4 No Model Differences Between Unipolar and Bipolar Disorders
7 Conclusions
References
Part III: Neurophysiological Methods
Chapter 4: The Concept of Event-Related Oscillations: A Spotlight on Extended Applications
Abbreviations
1 The Concept of Event-Related Oscillations
1.1 Conceptual Framework
1.1.1 Event-Related Potentials
1.1.2 Event-Related EEG Oscillations
1.2 Advantages
1.2.1 A Full Characterization of Event-Related EEG Signals
1.2.2 Evaluation of Parallel Processes in the Brain
1.2.3 A Physiological Approach to the Event-Related EEG Activity
2 Methodology
2.1 Analysis in the Frequency Domain
Cutting-edge and comprehensive, Computational Neuroscience is a valuable tool for researchers in the psychiatry and mental health fields who want to learn more about ways to incorporate computational approaches into utility and validity of clinical methods.
Table of contents :
Preface to the Series
Preface
Contents
Contributors
Part I: Introduction
Chapter 1: Toward Methodology for Strategic Innovations in Translational and Computational Neuroscience in Psychiatry
1 Background
2 Current Advancements
2.1 Future Research Goals
2.2 Expected Results
3 Impact
References
Part II: Molecular Methods
Chapter 2: Molecular Methods in Neuroscience and Psychiatry
1 Introduction
2 Methods
3 Results and Discussions
3.1 Methods in Neurotrannoscriptomics
3.2 Methods in Neuroproteomics
3.3 Methods in Epigenetics
3.4 Flow Cytometry in Psychiatry and Neuroscience
3.5 ELISpot/FluoroSpot in Psychiatry and Neuroscience
4 Conclusions
References
Chapter 3: Toward the Use of Research and Diagnostic Algorithmic Rules to Assess the Recurrence of Illness and Major Dysmood D...
1 Mood Disorder Concepts: The Ultimate Chaos
2 Diagnosis of Mood Disorders: The Ultimate Chaos
3 Lack of a Correct Model Prevents Targeted Research
4 Machine Learning Models
5 RADAR Scores and Plots
6 Why the Diagnosis ``Bipolar Disorder´´ Is Useless
6.1 Patients with BP1 and BP2 May Be Classified as SMDM or MDMD
6.2 Depressive and Manic Episodes Are Manifestations of ROI
6.3 The Diagnoses of MDD, MDE, BP1, and BP2 Are Irrelevant in Our Precision Models
6.4 No Model Differences Between Unipolar and Bipolar Disorders
7 Conclusions
References
Part III: Neurophysiological Methods
Chapter 4: The Concept of Event-Related Oscillations: A Spotlight on Extended Applications
Abbreviations
1 The Concept of Event-Related Oscillations
1.1 Conceptual Framework
1.1.1 Event-Related Potentials
1.1.2 Event-Related EEG Oscillations
1.2 Advantages
1.2.1 A Full Characterization of Event-Related EEG Signals
1.2.2 Evaluation of Parallel Processes in the Brain
1.2.3 A Physiological Approach to the Event-Related EEG Activity
2 Methodology
2.1 Analysis in the Frequency Domain
2.2 Analysis in the Time-Frequency Domain
2.2.1 Phase-Locked Power
2.2.2 Total Power
2.2.3 Temporal Phase Locking
Single-Sweep Wave Identification Method
Phase-Locking Factor
2.2.4 Event-Related Spatial Synchronization: Spatial Phase Locking
Phase-Locking Value
Phase-Lag Index
3 Extended Applications
3.1 Internal Information Processing
3.1.1 Response-Related Potentials
3.1.2 Coupling Between Slow Oscillations and Sleep Spindles
3.1.3 Additional Internal Potentials: A Future Focus of Research
3.2 Event-Related Frequency Tuning
3.3 Event-Related Spatial Synchronization
3.4 Multi-Second Behavioral Patterns
4 Concluding Remarks
References
Chapter 5: Quantitative EEG Analysis: Introduction and Basic Principles
1 Introduction
References
Chapter 6: QEEG and ERP Biomarkers of Psychotic and Mood Disorders and Their Treatment Response
1 The Perspective of Clinical Utility of Mismatch Negativity and P300 Event-Related Potentials in Psychotic Disorders
1.1 MMN
1.2 P300
2 Quantitative EEG Biomarkers of Depression and Antidepressant Treatment Response
References
Chapter 7: Quantitative EEG in Patients with Schizophrenia
1 Introduction
References
Part IV: Neuroimaging Methods
Chapter 8: Computational Anatomy Going Beyond Brain Morphometry
1 Introduction
2 Historical Overview
3 Computational Anatomy in Basic and Clinical Neuroscience
4 Limitations of Computational Anatomy Using T1-Weighted Data
5 Improved Brain Tissue Classification Using qMRI
6 ``In Vivo Histology´´ Using qMRI
7 Current Limitations of qMRI
8 Outlook
References
Chapter 9: Nonlinear Methods for the Investigation of Psychotic Disorders
1 Introduction
2 How to Evaluate Nonlinear Dynamical Systems?
3 Methods
4 Use Cases
5 Summary and Outlook
References
Chapter 10: Carving Out the Path to Computational Biomarkers for Mental Disorders
1 Introduction
1.1 The Complexity of Understanding Brain Function and Dysfunction
1.2 The Role of Emotions in Anxiety and Other Mental Disorders
1.3 The Role of the Amygdala in Emotions
1.4 Amygdala Activation and Connectivity in Anxiety and Other Mental Disorders
1.5 Structural and Functional Alterations in the Amygdala as a Possible Differential Diagnostic Biomarker for Mental Disorders
1.6 Real World Challenges for Amygdala-Based Biomarkers
2 Materials
2.1 Magnetic Resonance Imaging Hardware
2.2 Computing Hardware
3 Methods
3.1 Experimental Design: Amygdala Function
3.2 Experimental Design: Amygdala Anatomy
3.3 Experimental Design: Connectivity and Amygdala Regulation
3.4 Functional MRI of the Amygdala
3.5 Ultra-High Field Functional MRI of the Amygdala
4 Conclusion
References
Chapter 11: Neuroimaging Methods Investigating Anterior Insula Alterations in Schizophrenia and Mood Disorders
1 Introduction
2 Structural Changes
3 Functional Alterations
4 Impaired Connectivity
5 Conclusion
References
Chapter 12: Magnetic Resonance Spectroscopy
1 Introduction
1.1 Magnetic Resonance Spectroscopy: Principles
1.2 Clinical Applications of MRS
1.3 MRS Applications in Neurology
1.4 MRS Applications in Psychiatry
1.5 Functional MRS
2 Materials
3 Methods
3.1 Data Acquisition
3.1.1 Single Voxel Spectroscopy
3.1.2 MRS Imaging
3.1.3 Water and Lipid Suppression
3.2 Data Processing
4 Notes
4.1 Understanding Data Quality
4.2 Artifacts
4.3 Long and Short TE
References
Chapter 13: The Effect of Exogenous and Endogenous Parameters on Group Resting-State Effective Connectivity and BOLD Signal
1 Introduction
2 Methods
2.1 Participants
2.2 BETULA Data Collection
2.3 Data Analysis
2.3.1 Pre-processing
2.3.2 ROI Selection
2.3.3 DCM Analysis
3 Results
3.1 Effective Connectivity
3.2 Parameters Defining the BOLD Signal
4 Discussions
5 Conclusions
References
Chapter 14: Utility of Computational Approaches for Precision Psychiatry: Applications to Substance Use Disorders
1 Introduction
2.2.1 Phase-Locked Power
2.2.2 Total Power
2.2.3 Temporal Phase Locking
Single-Sweep Wave Identification Method
Phase-Locking Factor
2.2.4 Event-Related Spatial Synchronization: Spatial Phase Locking
Phase-Locking Value
Phase-Lag Index
3 Extended Applications
3.1 Internal Information Processing
3.1.1 Response-Related Potentials
3.1.2 Coupling Between Slow Oscillations and Sleep Spindles
3.1.3 Additional Internal Potentials: A Future Focus of Research
3.2 Event-Related Frequency Tuning
3.3 Event-Related Spatial Synchronization
3.4 Multi-Second Behavioral Patterns
4 Concluding Remarks
References
Chapter 5: Quantitative EEG Analysis: Introduction and Basic Principles
1 Introduction
References
Chapter 6: QEEG and ERP Biomarkers of Psychotic and Mood Disorders and Their Treatment Response
1 The Perspective of Clinical Utility of Mismatch Negativity and P300 Event-Related Potentials in Psychotic Disorders
1.1 MMN
1.2 P300
2 Quantitative EEG Biomarkers of Depression and Antidepressant Treatment Response
References
Chapter 7: Quantitative EEG in Patients with Schizophrenia
1 Introduction
References
Part IV: Neuroimaging Methods
Chapter 8: Computational Anatomy Going Beyond Brain Morphometry
1 Introduction
2 Historical Overview
3 Computational Anatomy in Basic and Clinical Neuroscience
4 Limitations of Computational Anatomy Using T1-Weighted Data
5 Improved Brain Tissue Classification Using qMRI
6 ``In Vivo Histology´´ Using qMRI
7 Current Limitations of qMRI
8 Outlook
References
Chapter 9: Nonlinear Methods for the Investigation of Psychotic Disorders
1 Introduction
2 How to Evaluate Nonlinear Dynamical Systems?
3 Methods
4 Use Cases
5 Summary and Outlook
References
Chapter 10: Carving Out the Path to Computational Biomarkers for Mental Disorders
1 Introduction
1.1 The Complexity of Understanding Brain Function and Dysfunction
1.2 The Role of Emotions in Anxiety and Other Mental Disorders
1.3 The Role of the Amygdala in Emotions
1.4 Amygdala Activation and Connectivity in Anxiety and Other Mental Disorders
1.5 Structural and Functional Alterations in the Amygdala as a Possible Differential Diagnostic Biomarker for Mental Disorders
1.6 Real World Challenges for Amygdala-Based Biomarkers
2 Materials
2.1 Magnetic Resonance Imaging Hardware
2.2 Computing Hardware
3 Methods
3.1 Experimental Design: Amygdala Function
3.2 Experimental Design: Amygdala Anatomy
3.3 Experimental Design: Connectivity and Amygdala Regulation
3.4 Functional MRI of the Amygdala
3.5 Ultra-High Field Functional MRI of the Amygdala
4 Conclusion
References
Chapter 11: Neuroimaging Methods Investigating Anterior Insula Alterations in Schizophrenia and Mood Disorders
1 Introduction
2 Structural Changes
3 Functional Alterations
4 Impaired Connectivity
5 Conclusion
References
Chapter 12: Magnetic Resonance Spectroscopy
1 Introduction
1.1 Magnetic Resonance Spectroscopy: Principles
1.2 Clinical Applications of MRS
1.3 MRS Applications in Neurology
1.4 MRS Applications in Psychiatry
1.5 Functional MRS
2 Materials
3 Methods
3.1 Data Acquisition
3.1.1 Single Voxel Spectroscopy
3.1.2 MRS Imaging
3.1.3 Water and Lipid Suppression
3.2 Data Processing
4 Notes
4.1 Understanding Data Quality
4.2 Artifacts
4.3 Long and Short TE
References
Chapter 13: The Effect of Exogenous and Endogenous Parameters on Group Resting-State Effective Connectivity and BOLD Signal
1 Introduction
2 Methods
2.1 Participants
2.2 BETULA Data Collection
2.3 Data Analysis
2.3.1 Pre-processing
2.3.2 ROI Selection
2.3.3 DCM Analysis
3 Results
3.1 Effective Connectivity
3.2 Parameters Defining the BOLD Signal
4 Discussions
5 Conclusions
References
Chapter 14: Utility of Computational Approaches for Precision Psychiatry: Applications to Substance Use Disorders
1 Introduction
2 Theory-Driven Approaches: Computational Modeling and Computational Phenotyping
2.1 Joint Modeling
3 Hybrid Approaches/Adaptive Design Optimization
4 Data-Driven Approaches/Machine Learning
5 Summary and Conclusion
References
Part V: Integrative Computational Neuroscience
Chapter 15: Multimodal Integration in Psychiatry: Clinical Potential and Challenges
1 Introduction
2 Materials
3 Methods
3.1 Multimodality of Magnetic Resonance Techniques
3.2 Multimodal Magnetic Resonance in the Study of Major Psychoses
3.3 Functional Neuroimaging and Neurophysiologic Techniques
3.3.1 Magnetic Resonance Imaging of the BOLD Effect
3.4 MRI Techniques Sensitive to Perfusion and Oxidative Metabolism
3.5 Optical Neuroimaging Techniques
3.6 Positron Emission Tomography
3.7 Electroencephalography
4 Conclusions
References
Chapter 16: Premises of Computational Neuroscience: Machine Learning Tools and Multivariate Analyses
1 Introduction
2 Guide to the Methodology
2.1 Overview
2.2 Mathematical Formulations
2.3 Benefits and Limitations of Using Multivariate Methods for Mental Health
3 Examples of Application of Multivariate Methods in Mental Health
3.1 Multivariate Methods Applied to the Classification of Schizophrenia
3.1.1 Objective
3.1.2 Data Used
3.1.3 Method Used
3.1.4 Results
3.2 Individual- and Group-Level Brain Signatures of Schizophrenia, Major Depressive, and Bipolar Disorders
3.2.1 Objective
3.2.2 Data
3.2.3 Method
3.2.4 Results
3.3 Multimodel Brain Signature with Task-fMRI, Resting State, and Morphometry in Schizophrenia and Major Depressive Disorder
3.3.1 Objective
3.3.2 Data
3.3.3 Methods
3.3.4 Results
4 Code and Toolbox Availability
5 Conclusion
References
Index
2.1 Joint Modeling
3 Hybrid Approaches/Adaptive Design Optimization
4 Data-Driven Approaches/Machine Learning
5 Summary and Conclusion
References
Part V: Integrative Computational Neuroscience
Chapter 15: Multimodal Integration in Psychiatry: Clinical Potential and Challenges
1 Introduction
2 Materials
3 Methods
3.1 Multimodality of Magnetic Resonance Techniques
3.2 Multimodal Magnetic Resonance in the Study of Major Psychoses
3.3 Functional Neuroimaging and Neurophysiologic Techniques
3.3.1 Magnetic Resonance Imaging of the BOLD Effect
3.4 MRI Techniques Sensitive to Perfusion and Oxidative Metabolism
3.5 Optical Neuroimaging Techniques
3.6 Positron Emission Tomography
3.7 Electroencephalography
4 Conclusions
References
Chapter 16: Premises of Computational Neuroscience: Machine Learning Tools and Multivariate Analyses
1 Introduction
2 Guide to the Methodology
2.1 Overview
2.2 Mathematical Formulations
2.3 Benefits and Limitations of Using Multivariate Methods for Mental Health
3 Examples of Application of Multivariate Methods in Mental Health
3.1 Multivariate Methods Applied to the Classification of Schizophrenia
3.1.1 Objective
3.1.2 Data Used
3.1.3 Method Used
3.1.4 Results
3.2 Individual- and Group-Level Brain Signatures of Schizophrenia, Major Depressive, and Bipolar Disorders
3.2.1 Objective
3.2.2 Data
3.2.3 Method
3.2.4 Results
3.3 Multimodel Brain Signature with Task-fMRI, Resting State, and Morphometry in Schizophrenia and Major Depressive Disorder
3.3.1 Objective
3.3.2 Data
3.3.3 Methods
3.3.4 Results
4 Code and Toolbox Availability
5 Conclusion
References
Index
Forwarded from National Brain Mapping Lab
🔰 آزمایشگاه ملی نقشه برداری مغز برگزار میکند:
💠 ۳۰ امین کارگاه ثبت، پردازش و تحلیل سیگنالهای EEG💠
🟢 مدرسین:
☑️دكتر علی مطيع نصرآبادی
☑️دكتر محمد ميكائيلی
🕑زمان: چهارشنبه، پنجشنبه و جمعه
۲۳ الی ۲۵ آبان ۱۴۰۳
‼️ظرفیت محدود ‼️
🌐برای ثبت نام و کسب اطلاعات بیشتر کلیک کنید.
☎️ تماس با ما: 02186093155
💠Telegram
💠Instagram
💠LinkedIn
🌐Website
💠 ۳۰ امین کارگاه ثبت، پردازش و تحلیل سیگنالهای EEG💠
🟢 مدرسین:
☑️دكتر علی مطيع نصرآبادی
☑️دكتر محمد ميكائيلی
🕑زمان: چهارشنبه، پنجشنبه و جمعه
۲۳ الی ۲۵ آبان ۱۴۰۳
‼️ظرفیت محدود ‼️
🌐برای ثبت نام و کسب اطلاعات بیشتر کلیک کنید.
☎️ تماس با ما: 02186093155
💠Telegram
🌐Website
https://www.aparat.com/v/z9565h0
کارگاه پردازش سیگنالهای مغزی دکتر نصرآبادی EEGWORKSHOP EEGLAB EEG/ERP
کارگاه پردازش سیگنالهای مغزی دکتر نصرآبادی EEGWORKSHOP EEGLAB EEG/ERP
آپارات - سرویس اشتراک ویدیو
کارگاه پردازش سیگنالهای مغزی دکتر نصرآبادی EEGWORKSHOP EEGLAB EEG/ERP
جلسه اول روز دوم کارگاه سه روزه پردازش سیگنالهای مغزی کاربرد در نوروساینس محاسباتی برگزار شده در آزمایشگاه نقشه برداری NBML توسط آقای دکتر نصرآبادیکانال تلگرام eegworkshop@
⭕ «دومین فراخوان حمایت از پایان نامه های تحصیلات تکمیلی »
پژوهشگاه ارتباطات و فناوری اطلاعات با هدف حمایت از توسعه پژوهشهای کاربردی، رصد فناوریهای نوین و حل چالشها و نیازهای کشور، بومیسازی محصولات و خدمات در راستای نیازمندیهای بخش فناوری اطلاعات و ارتباطات کشور و تشویق و بهرهمندی از ظرفیت اعضای هیأت علمی و دانشجویان تحصیلات تکمیلی دانشگاهها، مراکز آموزشی و پژوهشگاهها و با همکاری ستاد توسعه فناوریهای اتصالپذیری و ارتباطات معاونت علمی، فناوری و اقتصاد دانشبنیان ریاست جمهوری ، نسبت به حمایت از رسالهها و پایاننامههای تحصیلات تکمیلی (مقاطع کارشناسی ارشد و دکترا) اقدام مینماید.
این حمایت در راستای محورهای اولویتدار اعلامی از سوی پژوهشگاه و ستاد میباشد و مبالغ حمایتی برای دانشجویان ارشد ۲۴۰ میلیون ریال و برای دانشجویان دکتری ۷۲۰ میلیون ریال است.
ثبت نام از طریق:
http://egov.itrc.ac.ir/?q=fa/node/add/paper-suport
مهلت تا ۳۰ اسفند ۱۴۰۳
پژوهشگاه ارتباطات و فناوری اطلاعات با هدف حمایت از توسعه پژوهشهای کاربردی، رصد فناوریهای نوین و حل چالشها و نیازهای کشور، بومیسازی محصولات و خدمات در راستای نیازمندیهای بخش فناوری اطلاعات و ارتباطات کشور و تشویق و بهرهمندی از ظرفیت اعضای هیأت علمی و دانشجویان تحصیلات تکمیلی دانشگاهها، مراکز آموزشی و پژوهشگاهها و با همکاری ستاد توسعه فناوریهای اتصالپذیری و ارتباطات معاونت علمی، فناوری و اقتصاد دانشبنیان ریاست جمهوری ، نسبت به حمایت از رسالهها و پایاننامههای تحصیلات تکمیلی (مقاطع کارشناسی ارشد و دکترا) اقدام مینماید.
این حمایت در راستای محورهای اولویتدار اعلامی از سوی پژوهشگاه و ستاد میباشد و مبالغ حمایتی برای دانشجویان ارشد ۲۴۰ میلیون ریال و برای دانشجویان دکتری ۷۲۰ میلیون ریال است.
ثبت نام از طریق:
http://egov.itrc.ac.ir/?q=fa/node/add/paper-suport
مهلت تا ۳۰ اسفند ۱۴۰۳
Forwarded from mahdi
یک شرکت استارت آپی دانش بنیان جهت تکمیل تیم فنی خود به صورت پاره وقت از علاقمندان با مشخصات زیر دعوت به همکاری می نماید:
شرح وظایف:
- همکاری برای طراحی PCB
- همکاری برای برنامه نویسی C embedded روی میکروکنترلرها
- خرید قطعات الکترونیکی مورد نیاز
- لحیمکاری و مونتاژ قطعات روی PCB
- تست و عیبیابی اولیه مدارهای طراحی شده
- انجام کارهای مرتبط سخت افزاری در فرآیند تولید
مهارتها و شایستگیها:
- آشنایی با مفاهیم پایه طراحی مدارهای الکترونیکی
- توانایی خرید و آشنایی با قطعات الکترونیکی
- توانایی کار با ابزارهای لحیمکاری و تجهیزات تست
- آشنایی با طراحی PCB خصوصا نرم افزار Altium Designer و proteus
- آشنایی با زبان C
- کار و تسلط نسبی روی زبان پایتون
- کار با میکروکنترلر های STM33 و ATmega
شرایط مورد نیاز:
- دارای مدرک کارشناسی و یا ارشد در رشته های برق و الکترونیک، کامپیوتر و سخت افزار و یا مهندسی پزشکی
- آشنایی با دستگاه های الکترونیکی حوزه مغز و اعصاب مانند EEG
- تجربه کار با سیگنالهای مغزی (ترجیحا EEG)
- توانایی کار تیمی و برخورداری از مهارت حل مسئله
- توانایی کار کردن به شکل مستقل
- علاقمند به انجام پژوهش و مطالعه مقالات علمی
- کار به صورت پاره وقت و حداکثر ۳ روز در هفته با امکان تبدیل به موقعیت تمام وقت بسته به شرایط.
موقعیت مکانی شرکت:
تهران، سعادت آباد، خیابان خوردین
لطفا رزومه کاری خود را تا پایان روز ۸ آبانماه به آدرس ایمیل زیر ارسال کنید. داشتن رفرنس دانشگاهی یا صنعتی الزامیست mmdavari@gmail.com
شرح وظایف:
- همکاری برای طراحی PCB
- همکاری برای برنامه نویسی C embedded روی میکروکنترلرها
- خرید قطعات الکترونیکی مورد نیاز
- لحیمکاری و مونتاژ قطعات روی PCB
- تست و عیبیابی اولیه مدارهای طراحی شده
- انجام کارهای مرتبط سخت افزاری در فرآیند تولید
مهارتها و شایستگیها:
- آشنایی با مفاهیم پایه طراحی مدارهای الکترونیکی
- توانایی خرید و آشنایی با قطعات الکترونیکی
- توانایی کار با ابزارهای لحیمکاری و تجهیزات تست
- آشنایی با طراحی PCB خصوصا نرم افزار Altium Designer و proteus
- آشنایی با زبان C
- کار و تسلط نسبی روی زبان پایتون
- کار با میکروکنترلر های STM33 و ATmega
شرایط مورد نیاز:
- دارای مدرک کارشناسی و یا ارشد در رشته های برق و الکترونیک، کامپیوتر و سخت افزار و یا مهندسی پزشکی
- آشنایی با دستگاه های الکترونیکی حوزه مغز و اعصاب مانند EEG
- تجربه کار با سیگنالهای مغزی (ترجیحا EEG)
- توانایی کار تیمی و برخورداری از مهارت حل مسئله
- توانایی کار کردن به شکل مستقل
- علاقمند به انجام پژوهش و مطالعه مقالات علمی
- کار به صورت پاره وقت و حداکثر ۳ روز در هفته با امکان تبدیل به موقعیت تمام وقت بسته به شرایط.
موقعیت مکانی شرکت:
تهران، سعادت آباد، خیابان خوردین
لطفا رزومه کاری خود را تا پایان روز ۸ آبانماه به آدرس ایمیل زیر ارسال کنید. داشتن رفرنس دانشگاهی یا صنعتی الزامیست mmdavari@gmail.com